GLM enzymes with global test and dunnetts
library(tidyverse)
library(MASS)
library(multcomp)
library(tidyr)
library(vegan)
library(parallelDist)
library(ggordiplots)
library(lmtest)
library(psych)
library(lme4)
library(nlme)
library(multcomp)Read in data
total<-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/merged_metadata_herbicide_EPSCOR_2018.csv")
enz <- read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Enzymes/Enzyme_Summary_W_percent_Change.csv")
enz_only<-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Enzymes/Enzyme.csv")
wide_total <-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Wide_merged_GLM.csv")Split data by herbicide and time so we can look at each individually
#enz_only$Herbicide<-factor(enz_only$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = T)
enz_only$Herbicide<-factor(enz_only$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
str(enz_only$Total_Weed_Veg) int [1:60] 4 5 4 5 4 5 4 5 5 3 ...#checks out its numeric here.
Aatrex<-filter(enz_only, Herbicide == "Aatrex")
Clarity<-filter(enz_only, Herbicide == "Clarity")
Roundup<-filter(enz_only, Herbicide == "Roundup Powermax")
Handweed<-filter(enz_only, Herbicide != c("Aatrex"))
Handweed<-filter(Handweed, Herbicide != c("Roundup Powermax"))
Handweed<-filter(Handweed, Herbicide != c("Clarity"))
Handweed<-filter(Handweed, Herbicide != c("Non-Treated"))
NonTreat<-filter(enz_only, Herbicide == "Non-Treated")
Time1<-filter(enz_only, Time == "T1")
Time2<-filter(enz_only, Time == "T2")
Time3<-filter(enz_only, Time == "T3")
Time2_3<-filter(enz_only, Time != "T1")
Time1_2<-filter(enz_only, Time != "T3")Create summary tables
sub_enz_data<-enz_only[,c("Herbicide", "Time", "BG", "BX", "CBH", "AG", "NAG", "LAP", "PHOS" )]
enz_summary<-describeBy(sub_enz_data, list(sub_enz_data$Herbicide, sub_enz_data$Time), mat = T)
#write.csv(enz_summary, "/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Writing/Tables/enz_summary.csv")
#IC_summary2<-do.call("rbind", IC_summary)glm global test of enzymes time 1 against handweeded plots F stat for overall model signficance compared to intercept only model. Only if the more complex model is signficnat are the predictors kept. #https://statisticsbyjim.com/regression/interpret-f-test-overall-significance-regression/
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time1 <- Time1[!is.na(Time1$LAP),]
#sets order for handweed to be first category so dunnets compares to this.
sub_Time1$Herbicide<-factor(sub_Time1$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "PHOS"
[[2]]
DF_res DF F p-val
13.00000000 5.00000000 4.84099336 0.01016336
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
Deviance Residuals:
Min 1Q Median 3Q Max
-33.245 -13.607 3.091 12.108 21.406
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.788 19.209 3.217 0.006748 **
HerbicideNon-Treated 62.824 14.225 4.417 0.000696 ***
HerbicideAatrex 34.522 12.909 2.674 0.019106 *
HerbicideClarity 15.266 13.081 1.167 0.264136
HerbicideRoundup Powermax 25.729 13.081 1.967 0.070907 .
Total_Weed_Veg 8.447 4.225 1.999 0.066937 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 333.2754)
Null deviance: 12399.5 on 18 degrees of freedom
Residual deviance: 4332.6 on 13 degrees of freedom
AIC: 171.08
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
PHOS ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 4332.6 171.08
Herbicide 4 11717.4 181.98 5.5396 0.00792 **
Total_Weed_Veg 1 5664.6 174.17 3.9968 0.06694 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "PHOS"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Non-Treated - Hand == 0 62.82 14.22 4.417 <0.001 ***
Aatrex - Hand == 0 34.52 12.91 2.674 0.0268 *
Clarity - Hand == 0 15.27 13.08 1.167 0.5954
Roundup Powermax - Hand == 0 25.73 13.08 1.967 0.1552
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "PHOS"glm global test of enzymes time 1 agaist non-treated plots
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time1 <- Time1[!is.na(Time1$LAP),]
sub_Time1$Herbicide<-factor(sub_Time1$Herbicide, levels = c( "Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "PHOS"
[[2]]
DF_res DF F p-val
13.00000000 5.00000000 4.84099336 0.01016336
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
Deviance Residuals:
Min 1Q Median 3Q Max
-33.245 -13.607 3.091 12.108 21.406
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.612 17.592 7.084 8.25e-06 ***
HerbicideHand -62.824 14.225 -4.417 0.000696 ***
HerbicideAatrex -28.302 14.225 -1.990 0.068097 .
HerbicideClarity -47.558 13.961 -3.407 0.004683 **
HerbicideRoundup Powermax -37.095 13.961 -2.657 0.019743 *
Total_Weed_Veg 8.447 4.225 1.999 0.066937 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 333.2754)
Null deviance: 12399.5 on 18 degrees of freedom
Residual deviance: 4332.6 on 13 degrees of freedom
AIC: 171.08
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
PHOS ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 4332.6 171.08
Herbicide 4 11717.4 181.98 5.5396 0.00792 **
Total_Weed_Veg 1 5664.6 174.17 3.9968 0.06694 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "PHOS"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Hand - Non-Treated == 0 -62.82 14.22 -4.417 < 0.001 ***
Aatrex - Non-Treated == 0 -28.30 14.22 -1.990 0.13833
Clarity - Non-Treated == 0 -47.56 13.96 -3.407 0.00267 **
Roundup Powermax - Non-Treated == 0 -37.10 13.96 -2.657 0.02732 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "PHOS"#drop1(mod_herb, test = "F")adonis2 and graphs
#convert to long format for ggplot
subtime1_long <- sub_Time1[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )
data_long<-enz_only[,c("Herbicide", "Time","BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>%
gather(key = "Enzyme", value = "Activity", BG, CBH, PHOS, NAG, BX, AG, LAP )
dose.labs <- c("Time one","Time two","Time three")
names(dose.labs) <- c("T1", "T2", "T3")
phos_T1<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="PHOS")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") +
theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) + ylim(50,180) + theme(legend.position="none") + theme(strip.text.x = element_text(size = 12)) +ggtitle("PHOS Activity")
bgTime<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="BG")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") +
theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) + theme(legend.position="none") + theme(strip.text.x = element_text(size = 12))+ggtitle("BG Activity")
lapTime<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="LAP")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") +
theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) #+ theme(legend.position="none") + theme(strip.text.x = element_text(size = 12))+ggtitle("LAP Activity")
grid.arrange(phos_T1, bgTime, lapTime)Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
#multivariate
enz_table<-sub_Time1[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time1[,c("Herbicide", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Herbicide 4 0.073428 0.27006 1.4166 0.2258
Total_Weed_Veg 1 0.030014 0.11039 2.3162 0.1069
Residual 13 0.168454 0.61955
Total 18 0.271896 1.00000 time1_ord<-metaMDS(na.omit(sub_Time1[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.05397243
Run 1 stress 0.05565394
Run 2 stress 0.05565394
Run 3 stress 0.05021404
... New best solution
... Procrustes: rmse 0.05550926 max resid 0.2230549
Run 4 stress 0.04716523
... New best solution
... Procrustes: rmse 0.07032194 max resid 0.1840465
Run 5 stress 0.05397215
Run 6 stress 0.05021434
Run 7 stress 0.06394782
Run 8 stress 0.04718903
... Procrustes: rmse 0.01507886 max resid 0.04453192
Run 9 stress 0.04716627
... Procrustes: rmse 0.001072798 max resid 0.003680471
... Similar to previous best
Run 10 stress 0.0653428
Run 11 stress 0.05021841
Run 12 stress 0.04947881
Run 13 stress 0.04716608
... Procrustes: rmse 0.0010372 max resid 0.003560329
... Similar to previous best
Run 14 stress 0.06534227
Run 15 stress 0.05817447
Run 16 stress 0.06596012
Run 17 stress 0.05565396
Run 18 stress 0.05397199
Run 19 stress 0.04716526
... Procrustes: rmse 0.0005598519 max resid 0.001912569
... Similar to previous best
Run 20 stress 0.05565384
*** Best solution repeated 3 timesstressplot(time1_ord)
ord<-time1_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Herbicide")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Herbicide) + scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) + geom_point(aes(color= Herbicide))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22)) + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax"))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
Time 2 comparison to handweed
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time2 <- Time2[!is.na(Time2$LAP),]
sub_Time2$Herbicide<-factor(sub_Time2$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "BG"
[[2]]
DF_res DF F p-val
14.00000000 5.00000000 3.05831263 0.04515485
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
Deviance Residuals:
Min 1Q Median 3Q Max
-88.301 -40.676 5.253 49.887 71.405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 255.71 42.99 5.948 3.56e-05 ***
HerbicideNon-Treated -287.38 89.58 -3.208 0.00632 **
HerbicideAatrex -56.88 48.32 -1.177 0.25870
HerbicideClarity -165.26 48.32 -3.420 0.00414 **
HerbicideRoundup Powermax -72.49 46.78 -1.550 0.14355
Total_Weed_Veg 36.18 13.95 2.594 0.02122 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 4279.822)
Null deviance: 125363 on 19 degrees of freedom
Residual deviance: 59918 on 14 degrees of freedom
AIC: 230.86
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
BG ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 59918 230.86
Herbicide 4 123520 237.33 3.7153 0.02910 *
Total_Weed_Veg 1 88719 236.71 6.7295 0.02122 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "BG"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Non-Treated - Hand == 0 -287.38 89.58 -3.208 0.00533 **
Aatrex - Hand == 0 -56.88 48.32 -1.177 0.61041
Clarity - Hand == 0 -165.26 48.32 -3.420 0.00241 **
Roundup Powermax - Hand == 0 -72.49 46.78 -1.550 0.36117
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "BG"Time 2 comparison to non-treated
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time2 <- Time2[!is.na(Time2$LAP),]
sub_Time2$Herbicide<-factor(sub_Time2$Herbicide, levels = c( "Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "BG"
[[2]]
DF_res DF F p-val
14.00000000 5.00000000 3.05831263 0.04515485
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
Deviance Residuals:
Min 1Q Median 3Q Max
-88.301 -40.676 5.253 49.887 71.405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -31.68 109.60 -0.289 0.77681
HerbicideHand 287.38 89.58 3.208 0.00632 **
HerbicideAatrex 230.50 101.78 2.265 0.03993 *
HerbicideClarity 122.12 77.97 1.566 0.13960
HerbicideRoundup Powermax 214.89 95.62 2.247 0.04126 *
Total_Weed_Veg 36.18 13.95 2.594 0.02122 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 4279.822)
Null deviance: 125363 on 19 degrees of freedom
Residual deviance: 59918 on 14 degrees of freedom
AIC: 230.86
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
BG ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 59918 230.86
Herbicide 4 123520 237.33 3.7153 0.02910 *
Total_Weed_Veg 1 88719 236.71 6.7295 0.02122 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "BG"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Hand - Non-Treated == 0 287.38 89.58 3.208 0.00369 **
Aatrex - Non-Treated == 0 230.50 101.78 2.265 0.05083 .
Clarity - Non-Treated == 0 122.12 77.97 1.566 0.21851
Roundup Powermax - Non-Treated == 0 214.89 95.62 2.247 0.05304 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "BG"#convert to long format for ggplot
subTime2_long <- sub_Time2[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )
BG_T2<-ggplot(subTime2_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subtime1_long,Enzyme=="BG")) + theme_classic() + #facet_wrap(~ Enzyme, scales = "free_y") +
ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))
phos_T2<-ggplot(subTime2_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subTime2_long,Enzyme=="PHOS")) + theme_classic()+
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") +
theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 14)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + ylim(50,180)+ theme(legend.position="none") + xlab("Weed removal treatment")
#multivariate
enz_table<-sub_Time2[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time2[,c("Herbicide", "Treatment", "Total_Weed_Veg", "LQ_PW", "PW_Tot")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Herbicide 4 0.06391 0.19595 1.1261 0.36364
Total_Weed_Veg 1 0.06360 0.19501 4.4826 0.03397 *
Residual 14 0.19865 0.60904
Total 19 0.32616 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Time2_ord<-metaMDS(na.omit(sub_Time2[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.06830105
Run 1 stress 0.08079765
Run 2 stress 0.3727483
Run 3 stress 0.08117721
Run 4 stress 0.08902573
Run 5 stress 0.08068851
Run 6 stress 0.08510749
Run 7 stress 0.08113437
Run 8 stress 0.07255906
Run 9 stress 0.06830106
... Procrustes: rmse 1.130619e-05 max resid 3.771519e-05
... Similar to previous best
Run 10 stress 0.0861933
Run 11 stress 0.07104985
Run 12 stress 0.08105518
Run 13 stress 0.06807439
... New best solution
... Procrustes: rmse 0.01278134 max resid 0.04367879
Run 14 stress 0.07148786
Run 15 stress 0.07255902
Run 16 stress 0.07255895
Run 17 stress 0.07284964
Run 18 stress 0.08601962
Run 19 stress 0.08087363
Run 20 stress 0.07104982
*** Best solution was not repeated -- monoMDS stopping criteria:
20: stress ratio > sratmaxstressplot(Time2_ord)
ord<-Time2_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Total_Weed_Veg, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
Warning in chol.default(cov, pivot = TRUE) :
the matrix is either rank-deficient or indefinite
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
test$LQ_PW<-samp_data$LQ_PW
test$PW_Tot<-samp_data$PW_Tot
#test$Group=NULL
names(test)<-c("x", "y","Total Weedy Vegetation", "Sample_ID", "LQ_PW" , "PW_Tot")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y, size=`Total Weedy Vegetation`, color=PW_Tot)) +# scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) +
geom_point()
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22)) + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax"))
gWarning: Using size for a discrete variable is not advised.
Time 3 comparison to handweed
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time3 <- Time2[!is.na(Time3$LAP),]
sub_Time3$Herbicide<-factor(sub_Time3$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "PHOS"
[[2]]
DF_res DF F p-val
12.00000000 5.00000000 3.19459191 0.04606727
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
Deviance Residuals:
Min 1Q Median 3Q Max
-32.469 -13.845 5.629 10.478 29.181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 139.237 14.146 9.843 4.25e-07 ***
HerbicideNon-Treated -93.112 30.908 -3.013 0.0108 *
HerbicideAatrex -37.041 15.816 -2.342 0.0373 *
HerbicideClarity -35.644 15.816 -2.254 0.0437 *
HerbicideRoundup Powermax -9.086 16.624 -0.547 0.5947
Total_Weed_Veg 8.164 4.630 1.763 0.1033
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 457.4097)
Null deviance: 12795.1 on 17 degrees of freedom
Residual deviance: 5488.9 on 12 degrees of freedom
AIC: 168.04
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
PHOS ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 5488.9 168.04
Herbicide 4 12655.2 175.08 3.9168 0.02928 *
Total_Weed_Veg 1 6910.9 170.19 3.1087 0.10330
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "PHOS"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Non-Treated - Hand == 0 -93.112 30.908 -3.013 0.0101 *
Aatrex - Hand == 0 -37.041 15.816 -2.342 0.0691 .
Clarity - Hand == 0 -35.644 15.816 -2.254 0.0857 .
Roundup Powermax - Hand == 0 -9.086 16.624 -0.547 0.9590
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "PHOS"Time 3 to non-treated
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time3 <- Time2[!is.na(Time3$LAP),]
sub_Time3$Herbicide<-factor(sub_Time3$Herbicide, levels = c( "Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
##3rd model?
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF", "F", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_out[[1]]
[1] "PHOS"
[[2]]
DF_res DF F p-val
12.00000000 5.00000000 3.19459191 0.04606727
[[3]]
Call:
glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
Deviance Residuals:
Min 1Q Median 3Q Max
-32.469 -13.845 5.629 10.478 29.181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.124 37.586 1.227 0.2433
HerbicideHand 93.112 30.908 3.013 0.0108 *
HerbicideAatrex 56.071 34.925 1.605 0.1344
HerbicideClarity 57.468 27.088 2.122 0.0554 .
HerbicideRoundup Powermax 84.027 34.132 2.462 0.0299 *
Total_Weed_Veg 8.164 4.630 1.763 0.1033
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 457.4097)
Null deviance: 12795.1 on 17 degrees of freedom
Residual deviance: 5488.9 on 12 degrees of freedom
AIC: 168.04
Number of Fisher Scoring iterations: 2p.predict[[1]]
Single term deletions
Model:
PHOS ~ Herbicide + Total_Weed_Veg
Df Deviance AIC F value Pr(>F)
<none> 5488.9 168.04
Herbicide 4 12655.2 175.08 3.9168 0.02928 *
Total_Weed_Veg 1 6910.9 170.19 3.1087 0.10330
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[1] "PHOS"out[[1]]
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg",
sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
Hand - Non-Treated == 0 93.11 30.91 3.013 0.00633 **
Aatrex - Non-Treated == 0 56.07 34.93 1.605 0.20094
Clarity - Non-Treated == 0 57.47 27.09 2.122 0.06999 .
Roundup Powermax - Non-Treated == 0 84.03 34.13 2.462 0.03066 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
[[2]]
[1] "PHOS"#convert to long format for ggplot
subTime3_long <- sub_Time3[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )
PHOS_T3<-ggplot(subTime3_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subtime1_long,Enzyme=="PHOS")) + theme_classic() + #facet_wrap(~ Enzyme, scales = "free_y") +
ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("") + xlab("") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + ylim(50,180) + theme(legend.position="none")
#multivariate
enz_table<-sub_Time3[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time3[,c("Herbicide", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Herbicide 4 0.063414 0.20064 1.0298 0.43756
Total_Weed_Veg 1 0.067903 0.21484 4.4108 0.03896 *
Residual 12 0.184737 0.58451
Total 17 0.316054 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Time3_ord<-metaMDS(na.omit(sub_Time3[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.06454823
Run 1 stress 0.08916425
Run 2 stress 0.07144387
Run 3 stress 0.08795023
Run 4 stress 0.08820952
Run 5 stress 0.08595032
Run 6 stress 0.07753807
Run 7 stress 0.07990855
Run 8 stress 0.08271517
Run 9 stress 0.06464726
... Procrustes: rmse 0.03306517 max resid 0.08958612
Run 10 stress 0.06916108
Run 11 stress 0.08271518
Run 12 stress 0.06916115
Run 13 stress 0.06916109
Run 14 stress 0.08216972
Run 15 stress 0.0859902
Run 16 stress 0.06997775
Run 17 stress 0.06464723
... Procrustes: rmse 0.03306141 max resid 0.08950829
Run 18 stress 0.07907735
Run 19 stress 0.08271525
Run 20 stress 0.06916112
*** Best solution was not repeated -- monoMDS stopping criteria:
20: stress ratio > sratmaxstressplot(Time3_ord)
ord<-Time3_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Herbicide")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Herbicide) + scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) + geom_point(aes(color= Herbicide))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22)) + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax"))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
library(gridExtra)
grid.arrange(phos_T1, phos_T2, PHOS_T3, nrow=1)#Split by herbicide
All Together:
#Aatrex
#names(Aatrex)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
#Aatrex$Herbicide<-factor(Aatrex$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[!sub_enz_only$Time=="T1",]
#convert to long format for ggplot
subenz_long <- sub_enz_only[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )
ggplot(subenz_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("All samples by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
#multivariate
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Treatment + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Treatment + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Treatment 1 0.04473 0.10724 3.4103 0.04995 *
Total_Weed_Veg 1 0.00511 0.01226 0.3899 0.66933
Residual 28 0.36726 0.88050
Total 30 0.41710 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Aatrex_ord<-metaMDS(na.omit(sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.09605016
Run 1 stress 0.09515319
... New best solution
... Procrustes: rmse 0.04162516 max resid 0.186069
Run 2 stress 0.1119206
Run 3 stress 0.0995724
Run 4 stress 0.09455281
... New best solution
... Procrustes: rmse 0.02802476 max resid 0.1442835
Run 5 stress 0.1032063
Run 6 stress 0.1118813
Run 7 stress 0.1066645
Run 8 stress 0.09635027
Run 9 stress 0.1066642
Run 10 stress 0.1172666
Run 11 stress 0.115371
Run 12 stress 0.1145317
Run 13 stress 0.118284
Run 14 stress 0.09605015
Run 15 stress 0.1059765
Run 16 stress 0.1239954
Run 17 stress 0.09515321
Run 18 stress 0.1118813
Run 19 stress 0.1032063
Run 20 stress 0.1018694
*** Best solution was not repeated -- monoMDS stopping criteria:
19: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstressplot(Aatrex_ord)
ord<-Aatrex_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Aatrex by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
#adonis2 testing and pairwise comparisons for all t2 t3 samples
library(pairwiseAdonis)
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
#sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[!sub_enz_only$Time=="T1",]
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment")]
samp_data$Treatment<-as.factor(samp_data$Treatment)
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Treatment, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Treatment, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Treatment 4 0.06846 0.11686 1.0917 0.3606
Residual 33 0.51736 0.88314
Total 37 0.58583 1.00000 pairwise_treat<-pairwise.adonis2(psdist ~ Treatment, data = samp_data, perm=1000, p.adjust.methods="bonferroni")
#examination of changes over time.
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
#sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[sub_enz_only$Herbicide=="Hand",]
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time , data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.116937 0.60731 6.9594 0.001998 **
Residual 9 0.075613 0.39269
Total 11 0.192550 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1pairwise_time<-pairwise.adonis2(psdist ~ Time, data = samp_data, perm=1000, p.adjust.methods="bonferroni")
pairwise_time$parent_call
[1] "psdist ~ Time , strata = Null , permutations 999"
$T1_vs_T2
Df SumOfSqs R2 F Pr(>F)
Time 1 0.113421 0.74294 17.34 0.02897 *
Residual 6 0.039245 0.25706
Total 7 0.152666 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$T1_vs_T3
Df SumOfSqs R2 F Pr(>F)
Time 1 0.043672 0.43039 4.5336 0.04496 *
Residual 6 0.057799 0.56961
Total 7 0.101471 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$T2_vs_T3
Df SumOfSqs R2 F Pr(>F)
Time 1 0.018313 0.25261 2.0279 0.1778
Residual 6 0.054182 0.74739
Total 7 0.072495 1.00000
attr(,"class")
[1] "pwadstrata" "list" Aatrex with global test.
#Aatrex
#names(Aatrex)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
#Aatrex$Herbicide<-factor(Aatrex$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
sub_Aatrex <- Aatrex[!is.na(Aatrex$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Aatrex)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Aatrex)
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_outlist()p.predictlist()outlist()#convert to long format for ggplot
subAatrex_long <- sub_Aatrex[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP, C.N_enz )
#ggplot(subAatrex_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Aatrex by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")
aaUV1<-ggplot(subAatrex_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time") + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + theme(strip.text.x = element_text(size = 12)) #theme(legend.position="none") +
#multivariate
enz_table<-sub_Aatrex[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Aatrex[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.021240 0.13456 0.7286 0.5534
Total_Weed_Veg 1 0.019997 0.12668 1.3719 0.2657
Residual 8 0.116608 0.73875
Total 11 0.157845 1.00000 Aatrex_ord<-metaMDS(na.omit(sub_Aatrex[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.05299772
Run 1 stress 0.05299773
... Procrustes: rmse 5.056431e-05 max resid 0.0001084376
... Similar to previous best
Run 2 stress 0.0595568
Run 3 stress 0.0595568
Run 4 stress 0.05955681
Run 5 stress 0.05299772
... New best solution
... Procrustes: rmse 1.282965e-05 max resid 3.020205e-05
... Similar to previous best
Run 6 stress 0.05299773
... Procrustes: rmse 2.679368e-05 max resid 5.981211e-05
... Similar to previous best
Run 7 stress 0.05299772
... Procrustes: rmse 1.113708e-05 max resid 1.9283e-05
... Similar to previous best
Run 8 stress 0.05299772
... Procrustes: rmse 1.798482e-05 max resid 3.744637e-05
... Similar to previous best
Run 9 stress 0.06766168
Run 10 stress 0.05299772
... New best solution
... Procrustes: rmse 2.592028e-06 max resid 4.647295e-06
... Similar to previous best
Run 11 stress 0.05955681
Run 12 stress 0.2997668
Run 13 stress 0.05955684
Run 14 stress 0.05299774
... Procrustes: rmse 6.20017e-05 max resid 0.0001447245
... Similar to previous best
Run 15 stress 0.05299772
... Procrustes: rmse 9.487156e-06 max resid 2.388833e-05
... Similar to previous best
Run 16 stress 0.05299772
... Procrustes: rmse 1.713431e-05 max resid 4.013476e-05
... Similar to previous best
Run 17 stress 0.0595568
Run 18 stress 0.0595568
Run 19 stress 0.2830675
Run 20 stress 0.05299772
... Procrustes: rmse 3.820254e-06 max resid 6.920434e-06
... Similar to previous best
*** Best solution repeated 5 timesstressplot(Aatrex_ord)
ord<-Aatrex_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Aatrex by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
Clarity
#Clarity
#names(Clarity)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Clarity <- Clarity[!is.na(Clarity$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Clarity)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Clarity)
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_outlist()p.predictlist()outlist()#convert to long format for ggplot
subClarity_long <- sub_Clarity[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP,C.N_enz )
#ggplot(subClarity_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Clarity by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")
clUV1<-ggplot(subClarity_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time") + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + theme(strip.text.x = element_text(size = 12)) #theme(legend.position="none") +
#multivariate
enz_table<-sub_Clarity[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Clarity[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.009462 0.05188 0.2424 0.8142
Total_Weed_Veg 1 0.016798 0.09210 0.8608 0.3556
Residual 8 0.156124 0.85601
Total 11 0.182385 1.00000 Clarity_ord<-metaMDS(na.omit(sub_Clarity[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.02101681
Run 1 stress 0.02101685
... Procrustes: rmse 3.620412e-05 max resid 6.826508e-05
... Similar to previous best
Run 2 stress 0.0210168
... New best solution
... Procrustes: rmse 5.43583e-05 max resid 0.0001017165
... Similar to previous best
Run 3 stress 0.3243301
Run 4 stress 0.02101683
... Procrustes: rmse 7.375052e-05 max resid 0.0001365145
... Similar to previous best
Run 5 stress 0.0210168
... New best solution
... Procrustes: rmse 2.767795e-05 max resid 5.11665e-05
... Similar to previous best
Run 6 stress 0.02101682
... Procrustes: rmse 3.787603e-05 max resid 7.026581e-05
... Similar to previous best
Run 7 stress 0.02101681
... Procrustes: rmse 1.508813e-05 max resid 2.781328e-05
... Similar to previous best
Run 8 stress 0.02101682
... Procrustes: rmse 3.043025e-05 max resid 5.654389e-05
... Similar to previous best
Run 9 stress 0.0210168
... New best solution
... Procrustes: rmse 5.293131e-06 max resid 9.099501e-06
... Similar to previous best
Run 10 stress 0.07875293
Run 11 stress 0.3019399
Run 12 stress 0.0210168
... Procrustes: rmse 2.803025e-05 max resid 5.245232e-05
... Similar to previous best
Run 13 stress 0.02101682
... Procrustes: rmse 6.474392e-05 max resid 0.0001197917
... Similar to previous best
Run 14 stress 0.02101683
... Procrustes: rmse 4.766084e-05 max resid 8.822279e-05
... Similar to previous best
Run 15 stress 0.08802329
Run 16 stress 0.02101684
... Procrustes: rmse 5.665249e-05 max resid 0.0001056329
... Similar to previous best
Run 17 stress 0.02101683
... Procrustes: rmse 7.433211e-05 max resid 0.0001372225
... Similar to previous best
Run 18 stress 0.0210168
... New best solution
... Procrustes: rmse 4.285947e-06 max resid 8.796672e-06
... Similar to previous best
Run 19 stress 0.02103691
... Procrustes: rmse 0.002975831 max resid 0.007676425
... Similar to previous best
Run 20 stress 0.02101684
... Procrustes: rmse 6.23237e-05 max resid 0.0001168271
... Similar to previous best
*** Best solution repeated 3 timesstressplot(Clarity_ord)
ord<-Clarity_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Clarity by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
Handweed
subHandweed_long <- sub_Handweed[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity",AG,BG, PHOS, LAP, BX, C.N_enz )
HW_UV<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Handweed by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
subHandweed_long$Enzyme<-factor(subHandweed_long$Enzyme, levels = c("AG", "BG", "BX", "PHOS", "LAP", "C.N_enz"))
dose.labs <- c("AG","BG","BX","PHOS", "LAP", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "LAP", "C.N_enz")
HW_UV1<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
ggtitle("Handweeded") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time") + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + theme(strip.text.x = element_text(size = 12)) #theme(legend.position="none") +
#multivariate
enz_table<-sub_Handweed[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Handweed[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.116937 0.60731 6.6879 0.003996 **
Total_Weed_Veg 1 0.005673 0.02946 0.6490 0.514486
Residual 8 0.069940 0.36323
Total 11 0.192550 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1pairwise_time_handweed<-pairwise.adonis2(psdist ~ Time, data = samp_data, perm=1000, p.adjust.methods="bonferroni")
pairwise_time_handweed$parent_call
[1] "psdist ~ Time , strata = Null , permutations 999"
$T1_vs_T2
Df SumOfSqs R2 F Pr(>F)
Time 1 0.113421 0.74294 17.34 0.03097 *
Residual 6 0.039245 0.25706
Total 7 0.152666 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$T1_vs_T3
Df SumOfSqs R2 F Pr(>F)
Time 1 0.043672 0.43039 4.5336 0.04895 *
Residual 6 0.057799 0.56961
Total 7 0.101471 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$T2_vs_T3
Df SumOfSqs R2 F Pr(>F)
Time 1 0.018313 0.25261 2.0279 0.1638
Residual 6 0.054182 0.74739
Total 7 0.072495 1.00000
attr(,"class")
[1] "pwadstrata" "list" Handweed_ord<-metaMDS(na.omit(sub_Handweed[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.04799702
Run 1 stress 0.05647468
Run 2 stress 0.06915193
Run 3 stress 0.0745788
Run 4 stress 0.05647466
Run 5 stress 0.06915193
Run 6 stress 0.05647465
Run 7 stress 0.0571766
Run 8 stress 0.07467798
Run 9 stress 0.05647468
Run 10 stress 0.06968682
Run 11 stress 0.04799702
... Procrustes: rmse 1.977542e-05 max resid 3.052088e-05
... Similar to previous best
Run 12 stress 0.0678856
Run 13 stress 0.0782971
Run 14 stress 0.04799702
... Procrustes: rmse 1.371054e-05 max resid 2.134156e-05
... Similar to previous best
Run 15 stress 0.04799702
... Procrustes: rmse 2.073691e-05 max resid 3.130209e-05
... Similar to previous best
Run 16 stress 0.04799703
... Procrustes: rmse 3.710636e-05 max resid 5.736705e-05
... Similar to previous best
Run 17 stress 0.04799703
... Procrustes: rmse 4.75959e-05 max resid 7.3333e-05
... Similar to previous best
Run 18 stress 0.04799702
... Procrustes: rmse 3.05669e-05 max resid 4.756666e-05
... Similar to previous best
Run 19 stress 0.3361592
Run 20 stress 0.04799702
... Procrustes: rmse 9.463679e-06 max resid 1.456435e-05
... Similar to previous best
*** Best solution repeated 7 timesstressplot(Handweed_ord)
ord<-Handweed_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F8766D", "#00BA38", "#619CFF", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#00BA38", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
m <- m + annotate("text", label="Sampling time:\np < 0.01", x = 0.12, y = -0.055)
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22)) + theme(axis.title.x = element_text(size=15)) + theme(axis.title.y = element_text(size=15)) + scale_fill_discrete(name = "Sampling time", labels = c("Time 1", "Time 2", "Time 3"))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
HW_MV<-g
#convert to long format for ggplot
subRoundup_long <- sub_Roundup[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity", AG, BG, BX, PHOS, LAP, C.N_enz )
subRoundup_long$Enzyme<-factor(subRoundup_long$Enzyme, levels = c("AG", "BG", "BX", "PHOS", "LAP", "C.N_enz"))
dose.labs <- c("AG","BG","BX","PHOS", "LAP", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "LAP", "C.N_enz")
RUUV1<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
ggtitle("Roundup Powermax") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time") + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + theme(strip.text.x = element_text(size = 12)) #theme(legend.position="none") +
#ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Roundup Powermax by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
Phos_RU<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot(data=subset(subRoundup_long, subRoundup_long["Enzyme"] == "PHOS" )) + theme_classic() + #facet_wrap(~ Edaphic, scales = "free_y") +
theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 15)) + ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 20)) + theme(axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12))+ ylab("Activity\n(nmol/hr/g dry soil)") + xlab("Sampling Time") + scale_fill_discrete(name = "Sampling time", labels = c("Time 1", "Time 2", "Time 3"))
#multivariate
enz_table<-sub_Roundup[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Roundup[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.022833 0.29916 1.7399 0.1588
Total_Weed_Veg 1 0.007560 0.09906 1.1522 0.3367
Residual 7 0.045931 0.60178
Total 10 0.076325 1.00000 Roundup_ord<-metaMDS(na.omit(sub_Roundup[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.05022728
Run 1 stress 0.04557361
... New best solution
... Procrustes: rmse 0.04274026 max resid 0.1005604
Run 2 stress 0.05022726
Run 3 stress 0.05026268
Run 4 stress 0.05021694
Run 5 stress 0.05021691
Run 6 stress 0.05685044
Run 7 stress 0.05026265
Run 8 stress 0.04656015
Run 9 stress 0.05026275
Run 10 stress 0.05021691
Run 11 stress 0.04656017
Run 12 stress 0.3122243
Run 13 stress 0.05077791
Run 14 stress 0.05026265
Run 15 stress 0.05022729
Run 16 stress 0.05022729
Run 17 stress 0.04557361
... New best solution
... Procrustes: rmse 3.026343e-05 max resid 7.698245e-05
... Similar to previous best
Run 18 stress 0.05022728
Run 19 stress 0.04656016
Run 20 stress 0.05702321
*** Best solution repeated 1 timesstressplot(Roundup_ord)
ord<-Roundup_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Roundup by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
#NonTreat
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_NonTreat <- NonTreat[!is.na(NonTreat$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()
for(i in 1:(length(enzymes))){
mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_NonTreat)
mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_NonTreat)
glob_sig<-anova(mod_inter, mod_herb, test = "F")
if(glob_sig$`Pr(>F)`[2]<=0.05){
test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
sum_model<-summary(mod_herb)
post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
# p<-anova(mod_herb, mod_inter)
# p.vals<-c(p.vals, list(p))
significance_predic<-drop1(mod_herb, test = "F")
out<-c(out, list(post_hoc), enzymes[i])
models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
p.predict <- c(p.predict, list(significance_predic), enzymes[i])
}
}
models_outlist()p.predictlist()outlist()#convert to long format for ggplot
subNonTreat_long <- sub_NonTreat[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP, C.N_enz )
dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")
aaUV1<-ggplot(subNonTreat_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time") + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12)) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + theme(strip.text.x = element_text(size = 12)) #theme(legend.position="none") +
#ggplot(subNonTreat_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Non-treated by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))
#multivariate
enz_table<-sub_NonTreat[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_NonTreat[,c("Time", "Treatment", "Total_Weed_Veg")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 1000
adonis2(formula = psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)
Df SumOfSqs R2 F Pr(>F)
Time 2 0.037529 0.14368 0.6666 0.5764
Total_Weed_Veg 1 0.054778 0.20971 1.9460 0.1678
Residual 6 0.168895 0.64661
Total 9 0.261202 1.00000 NonTreat_ord<-metaMDS(na.omit(sub_NonTreat[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )Square root transformation
Wisconsin double standardization
Run 0 stress 0.006930743
Run 1 stress 0.006930712
... New best solution
... Procrustes: rmse 1.387962e-05 max resid 2.359383e-05
... Similar to previous best
Run 2 stress 0.03142679
Run 3 stress 0.006930747
... Procrustes: rmse 1.835222e-05 max resid 3.38146e-05
... Similar to previous best
Run 4 stress 0.007811699
Run 5 stress 0.008212288
Run 6 stress 0.007027435
... Procrustes: rmse 0.009637295 max resid 0.0197818
Run 7 stress 0.006930712
... New best solution
... Procrustes: rmse 7.202057e-07 max resid 1.147143e-06
... Similar to previous best
Run 8 stress 0.008221212
Run 9 stress 0.006933077
... Procrustes: rmse 0.0005560525 max resid 0.001086162
... Similar to previous best
Run 10 stress 0.00706276
... Procrustes: rmse 0.01270191 max resid 0.0261196
Run 11 stress 0.006983402
... Procrustes: rmse 0.002975934 max resid 0.005811273
... Similar to previous best
Run 12 stress 0.008212476
Run 13 stress 0.007044837
... Procrustes: rmse 0.01118192 max resid 0.02297606
Run 14 stress 0.006930698
... New best solution
... Procrustes: rmse 1.357542e-05 max resid 2.640974e-05
... Similar to previous best
Run 15 stress 0.008245814
Run 16 stress 0.008209322
Run 17 stress 0.006975915
... Procrustes: rmse 0.004874059 max resid 0.009966654
... Similar to previous best
Run 18 stress 0.008247181
Run 19 stress 0.006976058
... Procrustes: rmse 0.004841116 max resid 0.00989842
... Similar to previous best
Run 20 stress 0.02301761
*** Best solution repeated 3 timesstressplot(NonTreat_ord)
ord<-NonTreat_ord
#make the plot with everything
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)
#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")
#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time) + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0)
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("NonTreat by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))
gWarning in eval_tidy(x[[2]], data, env) :
restarting interrupted promise evaluation
combine figures for manuscirpt
subRoundup_long <- sub_Roundup[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity", AG, BG, BX, PHOS, C.N_enz )
RU_UV<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Glyphosate") + theme(plot.title= element_text( hjust = 0.5, size =17)) + ylab("") + xlab("") + scale_fill_discrete(name = "Sampling time", labels = c("Pre-treatment", "10-days post", "20-days post")) + theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) + theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) + theme(axis.text.y = element_text(size=14, angle=45)) + theme(axis.title.y = element_text(size=15))
subHandweed_long <- sub_Handweed[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>%
gather(key = "Enzyme", value = "Activity",, AG, BG, BX, PHOS, C.N_enz )
HW_UV<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Handweeded") + theme(plot.title= element_text( hjust = 0.5, size = 17)) + ylab("") + xlab("") + scale_fill_discrete(name = "Sampling time", labels = c("Pre-treatment", "10 days post", "20 days post")) + theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12)) + theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) + theme(axis.text.y = element_text(size=14, angle=45)) + theme(axis.title.y = element_text(size=15)) + theme(legend.position = "none")
#HW_MV <- HW_MV + theme(legend.position = "none") + ggtitle("Hand-weeded") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + theme(axis.text.y = element_text(size=14, angle=0)) + theme(axis.text.x = element_text(size = 14))
my_legend <- get_legend(RU_UV)
as_ggplot(my_legend)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity_legend.pdf")Saving 7.29 x 4.51 in imageggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity_legend.eps")Saving 7.29 x 4.51 in image
RU_UV<-RU_UV+ theme(legend.position = "none")
RU_UV
ggarrange(HW_UV, RU_UV, nrow = 2, ncol = 1)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity.eps", width = 7, height = 10)ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity.pdf", width = 7, height = 10)
#All together with conservative mixed models
with time 1 values as a preditor and only time 2 and 3
```r
enz_only$block<-factor(substr(enz_only$Sample_ID, start = 1, stop = 1))
dat <- enz_only[!is.na(enz_only$LAP),]
#Good for shapiro test on residuals without log transform. I ran each model and checked the residuals for normailty using \shapiro.test(resid(test_model_lmer))\. All of the follwing passed the shapiro wilk test : BG, CBH, AG, BX, PHOS, LAP, NAG
dat<-dat[dat$Time != \T1\, ]
library(lmerTest)
<!-- rnb-source-end -->
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Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmerThe following object is masked from ‘package:stats’:
step
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```r
```r
test_model_lmer<-lmer(NAG ~ Herbicide + BG_T1 + Time + (1|block) + (1|Time), data = dat)
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<!-- rnb-output-begin 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 -->
Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : unable to evaluate scaled gradient Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : Hessian is numerically singular: parameters are not uniquely determined Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: -1.7e-17
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```r
```r
summary(test_model_lmer)
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Linear mixed model fit by REML. t-tests use Satterthwaite’s method [‘lmerModLmerTest’] Formula: NAG ~ Herbicide + BG_T1 + Time + (1 | block) + (1 | Time) Data: dat
REML criterion at convergence: 249.6
Scaled residuals: Min 1Q Median 3Q Max -1.44900 -0.63217 0.01442 0.71376 1.67407
Random effects: Groups Name Variance Std.Dev. block (Intercept)
13.3991 3.6605
Time (Intercept) 0.5592 0.7478
Residual 78.5677 8.8638
Number of obs: 38, groups: block, 4; Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|)
(Intercept) 19.559594 7.160331 28.873014 2.732 0.0106 * HerbicideHand
3.321771 4.603387 28.386165 0.722 0.4764
HerbicideAatrex -5.617401 4.821419 28.479105 -1.165 0.2536
HerbicideClarity -6.082686 4.668234 28.321706 -1.303 0.2031
HerbicideRoundup Powermax -5.890518 4.884379 28.338781 -1.206
0.2378
BG_T1 0.006456 0.034110 30.983067 0.189 0.8511
TimeT3 -1.459557 3.077405 28.284763 -0.474 0.6389
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
BG_T1 HerbicidHnd -0.360
HerbicdAtrx -0.075 0.502
HerbcdClrty -0.192 0.523 0.555
HrbcdRndpPw -0.141 0.497 0.548 0.535
BG_T1 -0.821 0.030 -0.299 -0.169 -0.223
TimeT3 -0.190 -0.044 -0.043 -0.044 -0.001 0.004 optimizer (nloptwrap)
convergence code: 0 (OK) unable to evaluate scaled gradient Hessian is
numerically singular: parameters are not uniquely determined
<!-- rnb-output-end -->
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```r
```r
summary(glht(test_model_lmer, mcp(Herbicide = \Tukey\, Time = \Tukey\)))
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Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = NAG ~ Herbicide + BG_T1 + Time + (1 | block) + (1 | Time), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Herbicide: Hand - Non-Treated == 0 3.3218 4.6034 0.722 0.974 Herbicide: Aatrex - Non-Treated == 0 -5.6174 4.8214 -1.165 0.827 Herbicide: Clarity - Non-Treated == 0 -6.0827 4.6682 -1.303 0.749 Herbicide: Roundup Powermax - Non-Treated == 0 -5.8905 4.8844 -1.206 0.805 Herbicide: Aatrex - Hand == 0 -8.9392 4.7054 -1.900 0.356 Herbicide: Clarity - Hand == 0 -9.4045 4.5281 -2.077 0.259 Herbicide: Roundup Powermax - Hand == 0 -9.2123 4.7633 -1.934 0.336 Herbicide: Clarity - Aatrex == 0 -0.4653 4.4797 -0.104 1.000 Herbicide: Roundup Powermax - Aatrex == 0 -0.2731 4.6145 -0.059 1.000 Herbicide: Roundup Powermax - Clarity == 0 0.1922 4.6112 0.042 1.000 Time: T3 - T2 == 0 -1.4596 3.0774 -0.474 0.996 (Adjusted p values reported – single-step method)
<!-- rnb-output-end -->
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```r
```r
anova(test_model_lmer)
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Type III Analysis of Variance Table with Satterthwaite’s method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Herbicide 497.27 124.316 4 28.408 1.5823 0.2061 BG_T1 2.81 2.814 1 30.983 0.0358 0.8511 Time 17.67 17.673 1 28.285 0.2249 0.6389
<!-- rnb-output-end -->
<!-- rnb-source-begin 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 -->
```r
```r
enzymes<- c(\BG\, \CBH\, \PHOS\, \NAG\, \BX\, \AG\, \LAP\)
enzymes_t1<-c(\BG_T1\, \CBH_T1\, \PHOS_T1\, \NAG_T1\, \BX_T1\, \AG_T1\, \LAP_T1\)
dat#univariate
out<-list()
models_out<-list()
p.vals<-list()
for(i in 1:(length(enzymes))){
mod <- lmer(formula = noquote(paste(enzymes[i], \~ Herbicide + Total_Weed_Veg +\, enzymes_t1[i], \+ Time + Time*Herbicide + (1|Time)\, sep = \\)), data = dat)
sum_model<-summary(mod)
post_hoc<-summary(glht(mod, mcp(Herbicide = \Tukey\, Time = \Tukey\)))
p<-anova(mod)
p.vals<-c(p.vals, list(p))
out<-c(out, list(post_hoc))
models_out <- c(models_out, list(sum_model))
}
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : unable to evaluate scaled gradient Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : Hessian is numerically singular: parameters are not uniquely determined Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: 1.8e-13 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: -2.5e-09 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : unable to evaluate scaled gradient Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : Hessian is numerically singular: parameters are not uniquely determined Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: -3.8e-20 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: -8.2e-09 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : unable to evaluate scaled gradient Warning in checkConv(attr(opt, ), opt\(par, ctrl = control\)checkConv, : Hessian is numerically singular: parameters are not uniquely determined Warning in as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: -9.5e-20 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning: Model failed to converge with 1 negative eigenvalue: -4.6e-08 Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate Warning in mcp2matrix(model, linfct = linfct) : covariate interactions found – default contrast might be inappropriate
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubW9kZWxzX291dFxuYGBgXG5gYGAifQ== -->
```r
```r
models_out
<!-- rnb-source-end -->
<!-- rnb-output-begin {"data":"[[1]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 316.4\n\nScaled residuals: \n    Min      1Q  Median      3Q     Max \n-1.5621 -0.6573  0.1760  0.7593  1.3011 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)   42.19   6.496  \n Residual             3824.76  61.845  \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                  Estimate Std. Error        df t value Pr(>|t|)   \n(Intercept)                       135.3742    91.8431   26.0000   1.474  0.15249   \nHerbicideHand                     207.8733    70.3034   26.0000   2.957  0.00653 **\nHerbicideAatrex                   153.5361    78.4260   26.0000   1.958  0.06108 . \nHerbicideClarity                   66.2080    62.7177   26.0000   1.056  0.30084   \nHerbicideRoundup Powermax         140.0724    74.1925   26.0000   1.888  0.07024 . \nTotal_Weed_Veg                     22.2613     9.9879   26.0000   2.229  0.03468 * \nBG_T1                              -0.3553     0.2242   26.0000  -1.585  0.12504   \nTimeT3                             96.5564    50.5379   26.0000   1.911  0.06715 . \nHerbicideHand:TimeT3             -232.5015    73.3843   26.0000  -3.168  0.00390 **\nHerbicideAatrex:TimeT3           -139.6231    70.1109   26.0000  -1.991  0.05703 . \nHerbicideClarity:TimeT3           -78.4033    65.2379   26.0000  -1.202  0.24027   \nHerbicideRoundup Powermax:TimeT3 -148.3886    72.2611   26.0000  -2.054  0.05021 . \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V BG_T1  TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.813                                                                      \nHerbicdAtrx -0.773  0.815                                                               \nHerbcdClrty -0.733  0.774  0.789                                                        \nHrbcdRndpPw -0.780  0.811  0.834  0.785                                                 \nTotal_Wd_Vg -0.835  0.783  0.823  0.714  0.804                                          \nBG_T1       -0.467  0.061 -0.072 -0.036 -0.038  0.044                                   \nTimeT3      -0.492  0.505  0.481  0.511  0.491  0.299  0.076                            \nHrbcdHn:TT3  0.560 -0.746 -0.555 -0.546 -0.557 -0.478 -0.064 -0.747                     \nHrbcdAt:TT3  0.496 -0.503 -0.667 -0.495 -0.497 -0.394 -0.062 -0.750  0.624              \nHrbcdCl:TT3  0.310 -0.331 -0.309 -0.575 -0.319 -0.155 -0.055 -0.726  0.542  0.551       \nHrbcdRP:TT3  0.490 -0.482 -0.466 -0.471 -0.650 -0.372 -0.099 -0.727  0.602  0.591  0.535\noptimizer (nloptwrap) convergence code: 0 (OK)\nunable to evaluate scaled gradient\n Hessian is numerically singular: parameters are not uniquely determined\n\n\n[[2]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 231.7\n\nScaled residuals: \n     Min       1Q   Median       3Q      Max \n-1.80157 -0.64744  0.09158  0.62547  1.59895 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)   5.616   2.37   \n Residual             166.716  12.91   \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                  Estimate Std. Error        df t value Pr(>|t|)  \n(Intercept)                        1.52311   19.37200  26.00000   0.079   0.9379  \nHerbicideHand                     23.84971   15.32633  26.00000   1.556   0.1318  \nHerbicideAatrex                   16.75095   16.60502  26.00000   1.009   0.3224  \nHerbicideClarity                  15.00169   13.49783  26.00000   1.111   0.2766  \nHerbicideRoundup Powermax         24.05675   15.55516  26.00000   1.547   0.1341  \nTotal_Weed_Veg                     3.42058    2.12924  26.00000   1.606   0.1202  \nCBH_T1                             0.07912    0.24772  26.00000   0.319   0.7520  \nTimeT3                             9.92122   10.98133  26.00000   0.903   0.3746  \nHerbicideHand:TimeT3             -22.54987   15.47854  26.00000  -1.457   0.1571  \nHerbicideAatrex:TimeT3           -12.09944   14.75666  26.00000  -0.820   0.4197  \nHerbicideClarity:TimeT3          -14.76343   13.66256  26.00000  -1.081   0.2898  \nHerbicideRoundup Powermax:TimeT3 -30.71574   15.35080  26.00000  -2.001   0.0559 .\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V CBH_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.882                                                                      \nHerbicdAtrx -0.873  0.827                                                               \nHerbcdClrty -0.835  0.793  0.797                                                        \nHrbcdRndpPw -0.833  0.805  0.835  0.781                                                 \nTotal_Wd_Vg -0.887  0.792  0.835  0.730  0.807                                          \nCBH_T1      -0.473  0.294  0.181  0.245  0.099  0.207                                   \nTimeT3      -0.518  0.501  0.486  0.513  0.486  0.307  0.139                            \nHrbcdHn:TT3  0.593 -0.749 -0.574 -0.565 -0.567 -0.493 -0.156 -0.728                     \nHrbcdAt:TT3  0.526 -0.516 -0.682 -0.513 -0.507 -0.409 -0.141 -0.730  0.631              \nHrbcdCl:TT3  0.326 -0.341 -0.325 -0.581 -0.328 -0.169 -0.096 -0.705  0.547  0.555       \nHrbcdRP:TT3  0.531 -0.509 -0.496 -0.504 -0.661 -0.397 -0.209 -0.709  0.612  0.599  0.539\n\n[[3]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 264\n\nScaled residuals: \n     Min       1Q   Median       3Q      Max \n-1.99136 -0.49217  0.08396  0.44087  1.49734 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)   9.67    3.11   \n Residual             545.04   23.35   \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                 Estimate Std. Error       df t value Pr(>|t|)   \n(Intercept)                       23.8736    40.7882  26.0000   0.585  0.56339   \nHerbicideHand                     82.5759    27.6353  26.0000   2.988  0.00606 **\nHerbicideAatrex                   26.8757    29.5370  26.0000   0.910  0.37123   \nHerbicideClarity                  46.1868    24.3689  26.0000   1.895  0.06922 . \nHerbicideRoundup Powermax         57.7561    28.1963  26.0000   2.048  0.05075 . \nTotal_Weed_Veg                     4.2065     3.7715  26.0000   1.115  0.27491   \nPHOS_T1                            0.4259     0.2023  26.0000   2.105  0.04510 * \nTimeT3                            39.5134    19.2590  26.0000   2.052  0.05040 . \nHerbicideHand:TimeT3             -74.5215    27.6606  26.0000  -2.694  0.01220 * \nHerbicideAatrex:TimeT3           -41.0216    26.4366  26.0000  -1.552  0.13282   \nHerbicideClarity:TimeT3          -45.1302    24.6296  26.0000  -1.832  0.07838 . \nHerbicideRoundup Powermax:TimeT3 -81.4953    27.3321  26.0000  -2.982  0.00615 **\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V PHOS_T TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.828                                                                      \nHerbicdAtrx -0.703  0.795                                                               \nHerbcdClrty -0.776  0.792  0.772                                                        \nHrbcdRndpPw -0.754  0.810  0.831  0.785                                                 \nTotal_Wd_Vg -0.658  0.734  0.827  0.682  0.794                                          \nPHOS_T1     -0.658  0.285  0.024  0.239  0.122 -0.051                                   \nTimeT3      -0.434  0.495  0.484  0.511  0.494  0.290  0.068                            \nHrbcdHn:TT3  0.473 -0.723 -0.563 -0.542 -0.561 -0.474 -0.033 -0.739                     \nHrbcdAt:TT3  0.424 -0.492 -0.675 -0.494 -0.502 -0.389 -0.040 -0.742  0.623              \nHrbcdCl:TT3  0.279 -0.331 -0.315 -0.574 -0.326 -0.150 -0.057 -0.719  0.541  0.551       \nHrbcdRP:TT3  0.454 -0.489 -0.476 -0.488 -0.662 -0.361 -0.117 -0.720  0.600  0.589  0.535\noptimizer (nloptwrap) convergence code: 0 (OK)\nunable to evaluate scaled gradient\n Hessian is numerically singular: parameters are not uniquely determined\n\n\n[[4]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 218.2\n\nScaled residuals: \n     Min       1Q   Median       3Q      Max \n-1.39417 -0.74352 -0.02944  0.69741  1.61264 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)  0.5249  0.7245  \n Residual             99.5654  9.9782  \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                  Estimate Std. Error        df t value Pr(>|t|)\n(Intercept)                        4.45233   13.74312  26.00000   0.324    0.749\nHerbicideHand                     13.74013   11.62111  26.00000   1.182    0.248\nHerbicideAatrex                    7.35952   12.72270  26.00000   0.578    0.568\nHerbicideClarity                   4.92634   10.31222  26.00000   0.478    0.637\nHerbicideRoundup Powermax          9.11446   11.99777  26.00000   0.760    0.454\nTotal_Weed_Veg                     1.88332    1.61137  26.00000   1.169    0.253\nNAG_T1                             0.06081    0.19274  26.00000   0.315    0.755\nTimeT3                             3.33956    8.07396  26.00000   0.414    0.683\nHerbicideHand:TimeT3              -5.46370   11.83174  26.00000  -0.462    0.648\nHerbicideAatrex:TimeT3            -5.48621   11.30399  26.00000  -0.485    0.632\nHerbicideClarity:TimeT3           -5.68790   10.51905  26.00000  -0.541    0.593\nHerbicideRoundup Powermax:TimeT3 -12.03283   11.66460  26.00000  -1.032    0.312\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V NAG_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.894                                                                      \nHerbicdAtrx -0.904  0.824                                                               \nHerbcdClrty -0.853  0.787  0.792                                                        \nHrbcdRndpPw -0.882  0.809  0.835  0.782                                                 \nTotal_Wd_Vg -0.891  0.771  0.827  0.710  0.808                                          \nNAG_T1      -0.306  0.226  0.126  0.196  0.077  0.043                                   \nTimeT3      -0.510  0.507  0.496  0.521  0.502  0.301  0.059                            \nHrbcdHn:TT3  0.587 -0.737 -0.564 -0.549 -0.563 -0.478 -0.052 -0.753                     \nHrbcdAt:TT3  0.520 -0.499 -0.675 -0.498 -0.503 -0.393 -0.050 -0.756  0.623              \nHrbcdCl:TT3  0.320 -0.330 -0.317 -0.575 -0.324 -0.155 -0.042 -0.732  0.542  0.550       \nHrbcdRP:TT3  0.510 -0.488 -0.484 -0.486 -0.660 -0.372 -0.104 -0.733  0.602  0.590  0.534\n\n[[5]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 262.5\n\nScaled residuals: \n     Min       1Q   Median       3Q      Max \n-1.44749 -0.59977  0.05377  0.67943  1.90419 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)  14.98    3.871  \n Residual             534.03   23.109  \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                 Estimate Std. Error       df t value Pr(>|t|)\n(Intercept)                       -6.0960    33.6554  26.0000  -0.181    0.858\nHerbicideHand                     41.4351    27.4362  26.0000   1.510    0.143\nHerbicideAatrex                   36.4853    29.3347  26.0000   1.244    0.225\nHerbicideClarity                  26.1729    24.0530  26.0000   1.088    0.287\nHerbicideRoundup Powermax         28.4205    27.7505  26.0000   1.024    0.315\nTotal_Weed_Veg                     4.9480     3.7444  26.0000   1.321    0.198\nBX_T1                              0.2821     0.3368  26.0000   0.837    0.410\nTimeT3                            10.3790    19.4030  26.0000   0.535    0.597\nHerbicideHand:TimeT3             -34.3051    27.4895  26.0000  -1.248    0.223\nHerbicideAatrex:TimeT3           -29.6941    26.2530  26.0000  -1.131    0.268\nHerbicideClarity:TimeT3           -4.9350    24.4030  26.0000  -0.202    0.841\nHerbicideRoundup Powermax:TimeT3 -31.0800    27.2739  26.0000  -1.140    0.265\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V BX_T1  TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.890                                                                      \nHerbicdAtrx -0.858  0.808                                                               \nHerbcdClrty -0.841  0.791  0.785                                                        \nHrbcdRndpPw -0.837  0.795  0.835  0.776                                                 \nTotal_Wd_Vg -0.866  0.771  0.830  0.716  0.808                                          \nBX_T1       -0.423  0.294  0.085  0.228  0.059  0.092                                   \nTimeT3      -0.509  0.495  0.481  0.510  0.486  0.296  0.099                            \nHrbcdHn:TT3  0.579 -0.737 -0.566 -0.555 -0.563 -0.481 -0.095 -0.730                     \nHrbcdAt:TT3  0.514 -0.504 -0.677 -0.504 -0.503 -0.397 -0.090 -0.733  0.626              \nHrbcdCl:TT3  0.320 -0.335 -0.319 -0.578 -0.325 -0.159 -0.072 -0.709  0.544  0.552       \nHrbcdRP:TT3  0.521 -0.501 -0.483 -0.497 -0.657 -0.379 -0.172 -0.711  0.605  0.593  0.536\noptimizer (nloptwrap) convergence code: 0 (OK)\nunable to evaluate scaled gradient\n Hessian is numerically singular: parameters are not uniquely determined\n\n\n[[6]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 256.3\n\nScaled residuals: \n    Min      1Q  Median      3Q     Max \n-1.1829 -0.7346 -0.1762  0.6056  2.0934 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)   0.4675  0.6838 \n Residual             422.3517 20.5512 \nNumber of obs: 38, groups:  Time, 2\n\nFixed effects:\n                                   Estimate Std. Error         df t value Pr(>|t|)\n(Intercept)                       1.046e+01  2.845e+01  7.249e-04   0.368    0.998\nHerbicideHand                     2.114e+01  2.395e+01  2.600e+01   0.883    0.386\nHerbicideAatrex                   1.404e+01  2.601e+01  2.600e+01   0.540    0.594\nHerbicideClarity                  1.052e+01  2.110e+01  2.600e+01   0.499    0.622\nHerbicideRoundup Powermax         1.923e+01  2.464e+01  2.600e+01   0.780    0.442\nTotal_Weed_Veg                    2.611e+00  3.321e+00  2.600e+01   0.786    0.439\nAG_T1                             1.141e-01  3.055e-01  2.600e+01   0.373    0.712\nTimeT3                            3.994e+00  1.658e+01  2.089e-05   0.241    1.000\nHerbicideHand:TimeT3             -1.646e+01  2.442e+01  2.600e+01  -0.674    0.506\nHerbicideAatrex:TimeT3           -4.603e+00  2.333e+01  2.600e+01  -0.197    0.845\nHerbicideClarity:TimeT3          -8.261e+00  2.170e+01  2.600e+01  -0.381    0.707\nHerbicideRoundup Powermax:TimeT3 -2.115e+01  2.419e+01  2.600e+01  -0.874    0.390\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V AG_T1  TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.894                                                                      \nHerbicdAtrx -0.877  0.807                                                               \nHerbcdClrty -0.846  0.784  0.783                                                        \nHrbcdRndpPw -0.862  0.797  0.834  0.777                                                 \nTotal_Wd_Vg -0.891  0.773  0.829  0.715  0.807                                          \nAG_T1       -0.324  0.228  0.030  0.159  0.016  0.056                                   \nTimeT3      -0.519  0.517  0.497  0.530  0.502  0.305  0.099                            \nHrbcdHn:TT3  0.594 -0.742 -0.563 -0.554 -0.561 -0.479 -0.083 -0.759                     \nHrbcdAt:TT3  0.527 -0.505 -0.674 -0.504 -0.501 -0.395 -0.081 -0.762  0.625              \nHrbcdCl:TT3  0.328 -0.336 -0.316 -0.581 -0.322 -0.157 -0.072 -0.738  0.543  0.552       \nHrbcdRP:TT3  0.524 -0.496 -0.475 -0.490 -0.652 -0.374 -0.156 -0.740  0.604  0.592  0.536\n\n[[7]]\nLinear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']\nFormula: noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, enzymes_t1[i],  \n    \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\))\n   Data: dat\n\nREML criterion at convergence: 268.1\n\nScaled residuals: \n     Min       1Q   Median       3Q      Max \n-1.87044 -0.43088  0.00175  0.59139  1.82435 \n\nRandom effects:\n Groups   Name        Variance Std.Dev.\n Time     (Intercept)   30.93   5.561  \n Residual             1346.77  36.698  \nNumber of obs: 36, groups:  Time, 2\n\nFixed effects:\n                                  Estimate Std. Error        df t value Pr(>|t|)   \n(Intercept)                       102.4138    70.7750   24.0000   1.447  0.16082   \nHerbicideHand                      90.1302    47.1127   24.0000   1.913  0.06774 . \nHerbicideAatrex                    51.4495    51.4829   24.0000   0.999  0.32759   \nHerbicideClarity                   53.4286    42.3648   24.0000   1.261  0.21938   \nHerbicideRoundup Powermax          90.0871    49.3286   24.0000   1.826  0.08028 . \nTotal_Weed_Veg                     14.3874     6.2869   24.0000   2.288  0.03121 * \nLAP_T1                              0.1566     0.1565   24.0000   1.000  0.32709   \nTimeT3                            115.2575    36.8881   24.0000   3.125  0.00461 **\nHerbicideHand:TimeT3             -144.1412    49.5863   24.0000  -2.907  0.00773 **\nHerbicideAatrex:TimeT3            -92.9195    47.3212   24.0000  -1.964  0.06126 . \nHerbicideClarity:TimeT3          -102.5798    43.5894   24.0000  -2.353  0.02714 * \nHerbicideRoundup Powermax:TimeT3 -170.3214    48.5035   24.0000  -3.512  0.00179 **\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nCorrelation of Fixed Effects:\n            (Intr) HrbcdH HrbcdA HrbcdC HrbcRP Tt_W_V LAP_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3\nHerbicidHnd -0.793                                                                      \nHerbicdAtrx -0.692  0.839                                                               \nHerbcdClrty -0.774  0.827  0.812                                                        \nHrbcdRndpPw -0.779  0.854  0.855  0.830                                                 \nTotal_Wd_Vg -0.810  0.804  0.818  0.749  0.822                                          \nLAP_T1      -0.647  0.171 -0.024  0.181  0.130  0.195                                   \nTimeT3      -0.504  0.544  0.513  0.555  0.536  0.354  0.134                            \nHrbcdHn:TT3  0.571 -0.752 -0.589 -0.603 -0.607 -0.517 -0.149 -0.800                     \nHrbcdAt:TT3  0.518 -0.557 -0.674 -0.557 -0.554 -0.442 -0.137 -0.803  0.709              \nHrbcdCl:TT3  0.358 -0.402 -0.376 -0.598 -0.394 -0.228 -0.099 -0.782  0.640  0.648       \nHrbcdRP:TT3  0.518 -0.541 -0.513 -0.541 -0.677 -0.427 -0.164 -0.784  0.691  0.682  0.633\n"} -->
[[1]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 316.4
Scaled residuals: Min 1Q Median 3Q Max -1.5621 -0.6573 0.1760 0.7593 1.3011
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 42.19
6.496
Residual 3824.76 61.845
Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|)
(Intercept) 135.3742 91.8431 26.0000 1.474 0.15249
HerbicideHand 207.8733 70.3034 26.0000 2.957 0.00653
HerbicideAatrex 153.5361 78.4260 26.0000 1.958 0.06108 .
HerbicideClarity 66.2080 62.7177 26.0000 1.056 0.30084
HerbicideRoundup Powermax 140.0724 74.1925 26.0000 1.888 0.07024 .
Total_Weed_Veg 22.2613 9.9879 26.0000 2.229 0.03468 * BG_T1 -0.3553
0.2242 26.0000 -1.585 0.12504
TimeT3 96.5564 50.5379 26.0000 1.911 0.06715 . HerbicideHand:TimeT3
-232.5015 73.3843 26.0000 -3.168 0.00390
HerbicideAatrex:TimeT3 -139.6231 70.1109 26.0000 -1.991 0.05703 .
HerbicideClarity:TimeT3 -78.4033 65.2379 26.0000 -1.202 0.24027
HerbicideRoundup Powermax:TimeT3 -148.3886 72.2611 26.0000 -2.054
0.05021 . — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01
‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V BG_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.813
HerbicdAtrx -0.773 0.815
HerbcdClrty -0.733 0.774 0.789
HrbcdRndpPw -0.780 0.811 0.834 0.785
Total_Wd_Vg -0.835 0.783 0.823 0.714 0.804
BG_T1 -0.467 0.061 -0.072 -0.036 -0.038 0.044
TimeT3 -0.492 0.505 0.481 0.511 0.491 0.299 0.076
HrbcdHn:TT3 0.560 -0.746 -0.555 -0.546 -0.557 -0.478 -0.064 -0.747
HrbcdAt:TT3 0.496 -0.503 -0.667 -0.495 -0.497 -0.394 -0.062 -0.750
0.624
HrbcdCl:TT3 0.310 -0.331 -0.309 -0.575 -0.319 -0.155 -0.055 -0.726 0.542
0.551
HrbcdRP:TT3 0.490 -0.482 -0.466 -0.471 -0.650 -0.372 -0.099 -0.727 0.602
0.591 0.535 optimizer (nloptwrap) convergence code: 0 (OK) unable to
evaluate scaled gradient Hessian is numerically singular: parameters are
not uniquely determined
[[2]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 231.7
Scaled residuals: Min 1Q Median 3Q Max -1.80157 -0.64744 0.09158 0.62547 1.59895
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 5.616
2.37
Residual 166.716 12.91
Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.52311 19.37200 26.00000 0.079 0.9379
HerbicideHand 23.84971 15.32633 26.00000 1.556 0.1318
HerbicideAatrex 16.75095 16.60502 26.00000 1.009 0.3224
HerbicideClarity 15.00169 13.49783 26.00000 1.111 0.2766
HerbicideRoundup Powermax 24.05675 15.55516 26.00000 1.547 0.1341
Total_Weed_Veg 3.42058 2.12924 26.00000 1.606 0.1202
CBH_T1 0.07912 0.24772 26.00000 0.319 0.7520
TimeT3 9.92122 10.98133 26.00000 0.903 0.3746
HerbicideHand:TimeT3 -22.54987 15.47854 26.00000 -1.457 0.1571
HerbicideAatrex:TimeT3 -12.09944 14.75666 26.00000 -0.820 0.4197
HerbicideClarity:TimeT3 -14.76343 13.66256 26.00000 -1.081 0.2898
HerbicideRoundup Powermax:TimeT3 -30.71574 15.35080 26.00000 -2.001
0.0559 . — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01
‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V CBH_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.882
HerbicdAtrx -0.873 0.827
HerbcdClrty -0.835 0.793 0.797
HrbcdRndpPw -0.833 0.805 0.835 0.781
Total_Wd_Vg -0.887 0.792 0.835 0.730 0.807
CBH_T1 -0.473 0.294 0.181 0.245 0.099 0.207
TimeT3 -0.518 0.501 0.486 0.513 0.486 0.307 0.139
HrbcdHn:TT3 0.593 -0.749 -0.574 -0.565 -0.567 -0.493 -0.156 -0.728
HrbcdAt:TT3 0.526 -0.516 -0.682 -0.513 -0.507 -0.409 -0.141 -0.730
0.631
HrbcdCl:TT3 0.326 -0.341 -0.325 -0.581 -0.328 -0.169 -0.096 -0.705 0.547
0.555
HrbcdRP:TT3 0.531 -0.509 -0.496 -0.504 -0.661 -0.397 -0.209 -0.709 0.612
0.599 0.539
[[3]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 264
Scaled residuals: Min 1Q Median 3Q Max -1.99136 -0.49217 0.08396 0.44087 1.49734
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 9.67
3.11
Residual 545.04 23.35
Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|)
(Intercept) 23.8736 40.7882 26.0000 0.585 0.56339
HerbicideHand 82.5759 27.6353 26.0000 2.988 0.00606
HerbicideAatrex 26.8757 29.5370 26.0000 0.910 0.37123
HerbicideClarity 46.1868 24.3689 26.0000 1.895 0.06922 .
HerbicideRoundup Powermax 57.7561 28.1963 26.0000 2.048 0.05075 .
Total_Weed_Veg 4.2065 3.7715 26.0000 1.115 0.27491
PHOS_T1 0.4259 0.2023 26.0000 2.105 0.04510 * TimeT3 39.5134 19.2590
26.0000 2.052 0.05040 . HerbicideHand:TimeT3 -74.5215 27.6606 26.0000
-2.694 0.01220 * HerbicideAatrex:TimeT3 -41.0216 26.4366 26.0000 -1.552
0.13282
HerbicideClarity:TimeT3 -45.1302 24.6296 26.0000 -1.832 0.07838 .
HerbicideRoundup Powermax:TimeT3 -81.4953 27.3321 26.0000 -2.982 0.00615
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01
‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V PHOS_T TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.828
HerbicdAtrx -0.703 0.795
HerbcdClrty -0.776 0.792 0.772
HrbcdRndpPw -0.754 0.810 0.831 0.785
Total_Wd_Vg -0.658 0.734 0.827 0.682 0.794
PHOS_T1 -0.658 0.285 0.024 0.239 0.122 -0.051
TimeT3 -0.434 0.495 0.484 0.511 0.494 0.290 0.068
HrbcdHn:TT3 0.473 -0.723 -0.563 -0.542 -0.561 -0.474 -0.033 -0.739
HrbcdAt:TT3 0.424 -0.492 -0.675 -0.494 -0.502 -0.389 -0.040 -0.742
0.623
HrbcdCl:TT3 0.279 -0.331 -0.315 -0.574 -0.326 -0.150 -0.057 -0.719 0.541
0.551
HrbcdRP:TT3 0.454 -0.489 -0.476 -0.488 -0.662 -0.361 -0.117 -0.720 0.600
0.589 0.535 optimizer (nloptwrap) convergence code: 0 (OK) unable to
evaluate scaled gradient Hessian is numerically singular: parameters are
not uniquely determined
[[4]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 218.2
Scaled residuals: Min 1Q Median 3Q Max -1.39417 -0.74352 -0.02944 0.69741 1.61264
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 0.5249
0.7245
Residual 99.5654 9.9782
Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 4.45233 13.74312 26.00000 0.324 0.749 HerbicideHand 13.74013 11.62111 26.00000 1.182 0.248 HerbicideAatrex 7.35952 12.72270 26.00000 0.578 0.568 HerbicideClarity 4.92634 10.31222 26.00000 0.478 0.637 HerbicideRoundup Powermax 9.11446 11.99777 26.00000 0.760 0.454 Total_Weed_Veg 1.88332 1.61137 26.00000 1.169 0.253 NAG_T1 0.06081 0.19274 26.00000 0.315 0.755 TimeT3 3.33956 8.07396 26.00000 0.414 0.683 HerbicideHand:TimeT3 -5.46370 11.83174 26.00000 -0.462 0.648 HerbicideAatrex:TimeT3 -5.48621 11.30399 26.00000 -0.485 0.632 HerbicideClarity:TimeT3 -5.68790 10.51905 26.00000 -0.541 0.593 HerbicideRoundup Powermax:TimeT3 -12.03283 11.66460 26.00000 -1.032 0.312
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V NAG_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.894
HerbicdAtrx -0.904 0.824
HerbcdClrty -0.853 0.787 0.792
HrbcdRndpPw -0.882 0.809 0.835 0.782
Total_Wd_Vg -0.891 0.771 0.827 0.710 0.808
NAG_T1 -0.306 0.226 0.126 0.196 0.077 0.043
TimeT3 -0.510 0.507 0.496 0.521 0.502 0.301 0.059
HrbcdHn:TT3 0.587 -0.737 -0.564 -0.549 -0.563 -0.478 -0.052 -0.753
HrbcdAt:TT3 0.520 -0.499 -0.675 -0.498 -0.503 -0.393 -0.050 -0.756
0.623
HrbcdCl:TT3 0.320 -0.330 -0.317 -0.575 -0.324 -0.155 -0.042 -0.732 0.542
0.550
HrbcdRP:TT3 0.510 -0.488 -0.484 -0.486 -0.660 -0.372 -0.104 -0.733 0.602
0.590 0.534
[[5]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 262.5
Scaled residuals: Min 1Q Median 3Q Max -1.44749 -0.59977 0.05377 0.67943 1.90419
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 14.98
3.871
Residual 534.03 23.109
Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) -6.0960 33.6554 26.0000 -0.181 0.858 HerbicideHand 41.4351 27.4362 26.0000 1.510 0.143 HerbicideAatrex 36.4853 29.3347 26.0000 1.244 0.225 HerbicideClarity 26.1729 24.0530 26.0000 1.088 0.287 HerbicideRoundup Powermax 28.4205 27.7505 26.0000 1.024 0.315 Total_Weed_Veg 4.9480 3.7444 26.0000 1.321 0.198 BX_T1 0.2821 0.3368 26.0000 0.837 0.410 TimeT3 10.3790 19.4030 26.0000 0.535 0.597 HerbicideHand:TimeT3 -34.3051 27.4895 26.0000 -1.248 0.223 HerbicideAatrex:TimeT3 -29.6941 26.2530 26.0000 -1.131 0.268 HerbicideClarity:TimeT3 -4.9350 24.4030 26.0000 -0.202 0.841 HerbicideRoundup Powermax:TimeT3 -31.0800 27.2739 26.0000 -1.140 0.265
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V BX_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.890
HerbicdAtrx -0.858 0.808
HerbcdClrty -0.841 0.791 0.785
HrbcdRndpPw -0.837 0.795 0.835 0.776
Total_Wd_Vg -0.866 0.771 0.830 0.716 0.808
BX_T1 -0.423 0.294 0.085 0.228 0.059 0.092
TimeT3 -0.509 0.495 0.481 0.510 0.486 0.296 0.099
HrbcdHn:TT3 0.579 -0.737 -0.566 -0.555 -0.563 -0.481 -0.095 -0.730
HrbcdAt:TT3 0.514 -0.504 -0.677 -0.504 -0.503 -0.397 -0.090 -0.733
0.626
HrbcdCl:TT3 0.320 -0.335 -0.319 -0.578 -0.325 -0.159 -0.072 -0.709 0.544
0.552
HrbcdRP:TT3 0.521 -0.501 -0.483 -0.497 -0.657 -0.379 -0.172 -0.711 0.605
0.593 0.536 optimizer (nloptwrap) convergence code: 0 (OK) unable to
evaluate scaled gradient Hessian is numerically singular: parameters are
not uniquely determined
[[6]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 256.3
Scaled residuals: Min 1Q Median 3Q Max -1.1829 -0.7346 -0.1762 0.6056 2.0934
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 0.4675 0.6838 Residual 422.3517 20.5512 Number of obs: 38, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 1.046e+01 2.845e+01 7.249e-04 0.368 0.998 HerbicideHand 2.114e+01 2.395e+01 2.600e+01 0.883 0.386 HerbicideAatrex 1.404e+01 2.601e+01 2.600e+01 0.540 0.594 HerbicideClarity 1.052e+01 2.110e+01 2.600e+01 0.499 0.622 HerbicideRoundup Powermax 1.923e+01 2.464e+01 2.600e+01 0.780 0.442 Total_Weed_Veg 2.611e+00 3.321e+00 2.600e+01 0.786 0.439 AG_T1 1.141e-01 3.055e-01 2.600e+01 0.373 0.712 TimeT3 3.994e+00 1.658e+01 2.089e-05 0.241 1.000 HerbicideHand:TimeT3 -1.646e+01 2.442e+01 2.600e+01 -0.674 0.506 HerbicideAatrex:TimeT3 -4.603e+00 2.333e+01 2.600e+01 -0.197 0.845 HerbicideClarity:TimeT3 -8.261e+00 2.170e+01 2.600e+01 -0.381 0.707 HerbicideRoundup Powermax:TimeT3 -2.115e+01 2.419e+01 2.600e+01 -0.874 0.390
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V AG_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.894
HerbicdAtrx -0.877 0.807
HerbcdClrty -0.846 0.784 0.783
HrbcdRndpPw -0.862 0.797 0.834 0.777
Total_Wd_Vg -0.891 0.773 0.829 0.715 0.807
AG_T1 -0.324 0.228 0.030 0.159 0.016 0.056
TimeT3 -0.519 0.517 0.497 0.530 0.502 0.305 0.099
HrbcdHn:TT3 0.594 -0.742 -0.563 -0.554 -0.561 -0.479 -0.083 -0.759
HrbcdAt:TT3 0.527 -0.505 -0.674 -0.504 -0.501 -0.395 -0.081 -0.762
0.625
HrbcdCl:TT3 0.328 -0.336 -0.316 -0.581 -0.322 -0.157 -0.072 -0.738 0.543
0.552
HrbcdRP:TT3 0.524 -0.496 -0.475 -0.490 -0.652 -0.374 -0.156 -0.740 0.604
0.592 0.536
[[7]] Linear mixed model fit by REML. t-tests use Satterthwaite’s
method [‘lmerModLmerTest’] Formula: noquote(paste(enzymes[i], ~
Herbicide + Total_Weed_Veg +, enzymes_t1[i],
+ Time + Time*Herbicide + (1|Time), sep = \)) Data: dat
REML criterion at convergence: 268.1
Scaled residuals: Min 1Q Median 3Q Max -1.87044 -0.43088 0.00175 0.59139 1.82435
Random effects: Groups Name Variance Std.Dev. Time (Intercept) 30.93
5.561
Residual 1346.77 36.698
Number of obs: 36, groups: Time, 2
Fixed effects: Estimate Std. Error df t value Pr(>|t|)
(Intercept) 102.4138 70.7750 24.0000 1.447 0.16082
HerbicideHand 90.1302 47.1127 24.0000 1.913 0.06774 . HerbicideAatrex
51.4495 51.4829 24.0000 0.999 0.32759
HerbicideClarity 53.4286 42.3648 24.0000 1.261 0.21938
HerbicideRoundup Powermax 90.0871 49.3286 24.0000 1.826 0.08028 .
Total_Weed_Veg 14.3874 6.2869 24.0000 2.288 0.03121 * LAP_T1 0.1566
0.1565 24.0000 1.000 0.32709
TimeT3 115.2575 36.8881 24.0000 3.125 0.00461
HerbicideHand:TimeT3 -144.1412 49.5863 24.0000 -2.907 0.00773
HerbicideAatrex:TimeT3 -92.9195 47.3212 24.0000 -1.964 0.06126 .
HerbicideClarity:TimeT3 -102.5798 43.5894 24.0000 -2.353 0.02714 *
HerbicideRoundup Powermax:TimeT3 -170.3214 48.5035 24.0000 -3.512
0.00179 ** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01
‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects: (Intr) HrbcdH HrbcdA HrbcdC HrbcRP
Tt_W_V LAP_T1 TimeT3 HH:TT3 HA:TT3 HC:TT3 HerbicidHnd -0.793
HerbicdAtrx -0.692 0.839
HerbcdClrty -0.774 0.827 0.812
HrbcdRndpPw -0.779 0.854 0.855 0.830
Total_Wd_Vg -0.810 0.804 0.818 0.749 0.822
LAP_T1 -0.647 0.171 -0.024 0.181 0.130 0.195
TimeT3 -0.504 0.544 0.513 0.555 0.536 0.354 0.134
HrbcdHn:TT3 0.571 -0.752 -0.589 -0.603 -0.607 -0.517 -0.149 -0.800
HrbcdAt:TT3 0.518 -0.557 -0.674 -0.557 -0.554 -0.442 -0.137 -0.803
0.709
HrbcdCl:TT3 0.358 -0.402 -0.376 -0.598 -0.394 -0.228 -0.099 -0.782 0.640
0.648
HrbcdRP:TT3 0.518 -0.541 -0.513 -0.541 -0.677 -0.427 -0.164 -0.784 0.691
0.682 0.633
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxub3V0XG5gYGBcbmBgYCJ9 -->
```r
```r
out
<!-- rnb-source-end -->
<!-- rnb-output-begin {"data":"[[1]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)  \nHerbicide: Hand - Non-Treated == 0               207.87      70.30   2.957   0.0267 *\nHerbicide: Aatrex - Non-Treated == 0             153.54      78.43   1.958   0.3010  \nHerbicide: Clarity - Non-Treated == 0             66.21      62.72   1.056   0.8608  \nHerbicide: Roundup Powermax - Non-Treated == 0   140.07      74.19   1.888   0.3398  \nHerbicide: Aatrex - Hand == 0                    -54.34      45.94  -1.183   0.7950  \nHerbicide: Clarity - Hand == 0                  -141.67      45.33  -3.125   0.0158 *\nHerbicide: Roundup Powermax - Hand == 0          -67.80      44.59  -1.521   0.5776  \nHerbicide: Clarity - Aatrex == 0                 -87.33      48.19  -1.812   0.3850  \nHerbicide: Roundup Powermax - Aatrex == 0        -13.46      44.10  -0.305   0.9994  \nHerbicide: Roundup Powermax - Clarity == 0        73.86      46.22   1.598   0.5247  \nTime: T3 - T2 == 0                                96.56      50.54   1.911   0.3270  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n(Adjusted p values reported -- single-step method)\n\n\n[[2]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)\nHerbicide: Hand - Non-Treated == 0               23.850     15.326   1.556    0.554\nHerbicide: Aatrex - Non-Treated == 0             16.751     16.605   1.009    0.882\nHerbicide: Clarity - Non-Treated == 0            15.002     13.498   1.111    0.834\nHerbicide: Roundup Powermax - Non-Treated == 0   24.057     15.555   1.547    0.560\nHerbicide: Aatrex - Hand == 0                    -7.099      9.484  -0.749    0.964\nHerbicide: Clarity - Hand == 0                   -8.848      9.440  -0.937    0.910\nHerbicide: Roundup Powermax - Hand == 0           0.207      9.654   0.021    1.000\nHerbicide: Clarity - Aatrex == 0                 -1.749     10.040  -0.174    1.000\nHerbicide: Roundup Powermax - Aatrex == 0         7.306      9.305   0.785    0.956\nHerbicide: Roundup Powermax - Clarity == 0        9.055      9.811   0.923    0.916\nTime: T3 - T2 == 0                                9.921     10.981   0.903    0.922\n(Adjusted p values reported -- single-step method)\n\n\n[[3]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)  \nHerbicide: Hand - Non-Treated == 0                82.58      27.64   2.988   0.0243 *\nHerbicide: Aatrex - Non-Treated == 0              26.88      29.54   0.910   0.9206  \nHerbicide: Clarity - Non-Treated == 0             46.19      24.37   1.895   0.3371  \nHerbicide: Roundup Powermax - Non-Treated == 0    57.76      28.20   2.048   0.2556  \nHerbicide: Aatrex - Hand == 0                    -55.70      18.39  -3.029   0.0216 *\nHerbicide: Clarity - Hand == 0                   -36.39      17.06  -2.134   0.2160  \nHerbicide: Roundup Powermax - Hand == 0          -24.82      17.20  -1.443   0.6321  \nHerbicide: Clarity - Aatrex == 0                  19.31      18.86   1.024   0.8761  \nHerbicide: Roundup Powermax - Aatrex == 0         30.88      16.84   1.834   0.3732  \nHerbicide: Roundup Powermax - Clarity == 0        11.57      17.61   0.657   0.9795  \nTime: T3 - T2 == 0                                39.51      19.26   2.052   0.2538  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n(Adjusted p values reported -- single-step method)\n\n\n[[4]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)\nHerbicide: Hand - Non-Treated == 0               13.740     11.621   1.182    0.795\nHerbicide: Aatrex - Non-Treated == 0              7.360     12.723   0.578    0.988\nHerbicide: Clarity - Non-Treated == 0             4.926     10.312   0.478    0.995\nHerbicide: Roundup Powermax - Non-Treated == 0    9.114     11.998   0.760    0.961\nHerbicide: Aatrex - Hand == 0                    -6.381      7.308  -0.873    0.932\nHerbicide: Clarity - Hand == 0                   -8.814      7.262  -1.214    0.777\nHerbicide: Roundup Powermax - Hand == 0          -4.626      7.300  -0.634    0.982\nHerbicide: Clarity - Aatrex == 0                 -2.433      7.767  -0.313    0.999\nHerbicide: Roundup Powermax - Aatrex == 0         1.755      7.134   0.246    1.000\nHerbicide: Roundup Powermax - Clarity == 0        4.188      7.537   0.556    0.990\nTime: T3 - T2 == 0                                3.340      8.074   0.414    0.998\n(Adjusted p values reported -- single-step method)\n\n\n[[5]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)\nHerbicide: Hand - Non-Treated == 0               41.435     27.436   1.510    0.586\nHerbicide: Aatrex - Non-Treated == 0             36.485     29.335   1.244    0.761\nHerbicide: Clarity - Non-Treated == 0            26.173     24.053   1.088    0.846\nHerbicide: Roundup Powermax - Non-Treated == 0   28.421     27.751   1.024    0.876\nHerbicide: Aatrex - Hand == 0                    -4.950     17.667  -0.280    1.000\nHerbicide: Clarity - Hand == 0                  -15.262     16.960  -0.900    0.924\nHerbicide: Roundup Powermax - Hand == 0         -13.015     17.666  -0.737    0.966\nHerbicide: Clarity - Aatrex == 0                -10.312     18.209  -0.566    0.989\nHerbicide: Roundup Powermax - Aatrex == 0        -8.065     16.469  -0.490    0.995\nHerbicide: Roundup Powermax - Clarity == 0        2.248     17.696   0.127    1.000\nTime: T3 - T2 == 0                               10.379     19.403   0.535    0.992\n(Adjusted p values reported -- single-step method)\n\n\n[[6]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                               Estimate Std. Error z value Pr(>|z|)\nHerbicide: Hand - Non-Treated == 0               21.137     23.947   0.883    0.929\nHerbicide: Aatrex - Non-Treated == 0             14.036     26.005   0.540    0.991\nHerbicide: Clarity - Non-Treated == 0            10.525     21.095   0.499    0.994\nHerbicide: Roundup Powermax - Non-Treated == 0   19.228     24.640   0.780    0.957\nHerbicide: Aatrex - Hand == 0                    -7.101     15.621  -0.455    0.996\nHerbicide: Clarity - Hand == 0                  -10.612     15.053  -0.705    0.972\nHerbicide: Roundup Powermax - Hand == 0          -1.909     15.477  -0.123    1.000\nHerbicide: Clarity - Aatrex == 0                 -3.511     16.179  -0.217    1.000\nHerbicide: Roundup Powermax - Aatrex == 0         5.192     14.631   0.355    0.999\nHerbicide: Roundup Powermax - Clarity == 0        8.703     15.642   0.556    0.990\nTime: T3 - T2 == 0                                3.994     16.576   0.241    1.000\n(Adjusted p values reported -- single-step method)\n\n\n[[7]]\n\n\t Simultaneous Tests for General Linear Hypotheses\n\nMultiple Comparisons of Means: Tukey Contrasts\n\n\nFit: lmer(formula = noquote(paste(enzymes[i], \\~ Herbicide + Total_Weed_Veg +\\, \n    enzymes_t1[i], \\+ Time + Time*Herbicide + (1|Time)\\, sep = \\\\)), \n    data = dat)\n\nLinear Hypotheses:\n                                                Estimate Std. Error z value Pr(>|z|)  \nHerbicide: Hand - Non-Treated == 0              90.13018   47.11274   1.913   0.3221  \nHerbicide: Aatrex - Non-Treated == 0            51.44947   51.48287   0.999   0.8835  \nHerbicide: Clarity - Non-Treated == 0           53.42855   42.36481   1.261   0.7452  \nHerbicide: Roundup Powermax - Non-Treated == 0  90.08714   49.32864   1.826   0.3728  \nHerbicide: Aatrex - Hand == 0                  -38.68071   28.25435  -1.369   0.6761  \nHerbicide: Clarity - Hand == 0                 -36.70162   26.67552  -1.376   0.6715  \nHerbicide: Roundup Powermax - Hand == 0         -0.04304   26.18454  -0.002   1.0000  \nHerbicide: Clarity - Aatrex == 0                 1.97908   30.07986   0.066   1.0000  \nHerbicide: Roundup Powermax - Aatrex == 0       38.63767   27.23358   1.419   0.6428  \nHerbicide: Roundup Powermax - Clarity == 0      36.65858   27.57676   1.329   0.7020  \nTime: T3 - T2 == 0                             115.25748   36.88812   3.125   0.0155 *\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n(Adjusted p values reported -- single-step method)\n"} -->
[[1]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Herbicide: Hand - Non-Treated == 0 207.87 70.30 2.957 0.0267
Herbicide: Aatrex - Non-Treated == 0 153.54 78.43 1.958 0.3010
Herbicide: Clarity - Non-Treated == 0 66.21 62.72 1.056 0.8608
Herbicide: Roundup Powermax - Non-Treated == 0 140.07 74.19 1.888
0.3398
Herbicide: Aatrex - Hand == 0 -54.34 45.94 -1.183 0.7950
Herbicide: Clarity - Hand == 0 -141.67 45.33 -3.125 0.0158
Herbicide: Roundup Powermax - Hand == 0 -67.80 44.59 -1.521 0.5776
Herbicide: Clarity - Aatrex == 0 -87.33 48.19 -1.812 0.3850
Herbicide: Roundup Powermax - Aatrex == 0 -13.46 44.10 -0.305
0.9994
Herbicide: Roundup Powermax - Clarity == 0 73.86 46.22 1.598
0.5247
Time: T3 - T2 == 0 96.56 50.54 1.911 0.3270
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported – single-step method)
[[2]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Herbicide: Hand - Non-Treated == 0 23.850 15.326 1.556 0.554 Herbicide: Aatrex - Non-Treated == 0 16.751 16.605 1.009 0.882 Herbicide: Clarity - Non-Treated == 0 15.002 13.498 1.111 0.834 Herbicide: Roundup Powermax - Non-Treated == 0 24.057 15.555 1.547 0.560 Herbicide: Aatrex - Hand == 0 -7.099 9.484 -0.749 0.964 Herbicide: Clarity - Hand == 0 -8.848 9.440 -0.937 0.910 Herbicide: Roundup Powermax - Hand == 0 0.207 9.654 0.021 1.000 Herbicide: Clarity - Aatrex == 0 -1.749 10.040 -0.174 1.000 Herbicide: Roundup Powermax - Aatrex == 0 7.306 9.305 0.785 0.956 Herbicide: Roundup Powermax - Clarity == 0 9.055 9.811 0.923 0.916 Time: T3 - T2 == 0 9.921 10.981 0.903 0.922 (Adjusted p values reported – single-step method)
[[3]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Herbicide: Hand - Non-Treated == 0 82.58 27.64 2.988 0.0243
Herbicide: Aatrex - Non-Treated == 0 26.88 29.54 0.910 0.9206
Herbicide: Clarity - Non-Treated == 0 46.19 24.37 1.895 0.3371
Herbicide: Roundup Powermax - Non-Treated == 0 57.76 28.20 2.048
0.2556
Herbicide: Aatrex - Hand == 0 -55.70 18.39 -3.029 0.0216
Herbicide: Clarity - Hand == 0 -36.39 17.06 -2.134 0.2160
Herbicide: Roundup Powermax - Hand == 0 -24.82 17.20 -1.443 0.6321
Herbicide: Clarity - Aatrex == 0 19.31 18.86 1.024 0.8761
Herbicide: Roundup Powermax - Aatrex == 0 30.88 16.84 1.834 0.3732
Herbicide: Roundup Powermax - Clarity == 0 11.57 17.61 0.657
0.9795
Time: T3 - T2 == 0 39.51 19.26 2.052 0.2538
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported – single-step method)
[[4]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Herbicide: Hand - Non-Treated == 0 13.740 11.621 1.182 0.795 Herbicide: Aatrex - Non-Treated == 0 7.360 12.723 0.578 0.988 Herbicide: Clarity - Non-Treated == 0 4.926 10.312 0.478 0.995 Herbicide: Roundup Powermax - Non-Treated == 0 9.114 11.998 0.760 0.961 Herbicide: Aatrex - Hand == 0 -6.381 7.308 -0.873 0.932 Herbicide: Clarity - Hand == 0 -8.814 7.262 -1.214 0.777 Herbicide: Roundup Powermax - Hand == 0 -4.626 7.300 -0.634 0.982 Herbicide: Clarity - Aatrex == 0 -2.433 7.767 -0.313 0.999 Herbicide: Roundup Powermax - Aatrex == 0 1.755 7.134 0.246 1.000 Herbicide: Roundup Powermax - Clarity == 0 4.188 7.537 0.556 0.990 Time: T3 - T2 == 0 3.340 8.074 0.414 0.998 (Adjusted p values reported – single-step method)
[[5]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Herbicide: Hand - Non-Treated == 0 41.435 27.436 1.510 0.586 Herbicide: Aatrex - Non-Treated == 0 36.485 29.335 1.244 0.761 Herbicide: Clarity - Non-Treated == 0 26.173 24.053 1.088 0.846 Herbicide: Roundup Powermax - Non-Treated == 0 28.421 27.751 1.024 0.876 Herbicide: Aatrex - Hand == 0 -4.950 17.667 -0.280 1.000 Herbicide: Clarity - Hand == 0 -15.262 16.960 -0.900 0.924 Herbicide: Roundup Powermax - Hand == 0 -13.015 17.666 -0.737 0.966 Herbicide: Clarity - Aatrex == 0 -10.312 18.209 -0.566 0.989 Herbicide: Roundup Powermax - Aatrex == 0 -8.065 16.469 -0.490 0.995 Herbicide: Roundup Powermax - Clarity == 0 2.248 17.696 0.127 1.000 Time: T3 - T2 == 0 10.379 19.403 0.535 0.992 (Adjusted p values reported – single-step method)
[[6]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Herbicide: Hand - Non-Treated == 0 21.137 23.947 0.883 0.929 Herbicide: Aatrex - Non-Treated == 0 14.036 26.005 0.540 0.991 Herbicide: Clarity - Non-Treated == 0 10.525 21.095 0.499 0.994 Herbicide: Roundup Powermax - Non-Treated == 0 19.228 24.640 0.780 0.957 Herbicide: Aatrex - Hand == 0 -7.101 15.621 -0.455 0.996 Herbicide: Clarity - Hand == 0 -10.612 15.053 -0.705 0.972 Herbicide: Roundup Powermax - Hand == 0 -1.909 15.477 -0.123 1.000 Herbicide: Clarity - Aatrex == 0 -3.511 16.179 -0.217 1.000 Herbicide: Roundup Powermax - Aatrex == 0 5.192 14.631 0.355 0.999 Herbicide: Roundup Powermax - Clarity == 0 8.703 15.642 0.556 0.990 Time: T3 - T2 == 0 3.994 16.576 0.241 1.000 (Adjusted p values reported – single-step method)
[[7]]
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: lmer(formula = noquote(paste(enzymes[i], ~ Herbicide + Total_Weed_Veg +, enzymes_t1[i], + Time + Time*Herbicide + (1|Time), sep = \)), data = dat)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Herbicide: Hand - Non-Treated == 0 90.13018 47.11274 1.913 0.3221
Herbicide: Aatrex - Non-Treated == 0 51.44947 51.48287 0.999
0.8835
Herbicide: Clarity - Non-Treated == 0 53.42855 42.36481 1.261
0.7452
Herbicide: Roundup Powermax - Non-Treated == 0 90.08714 49.32864 1.826
0.3728
Herbicide: Aatrex - Hand == 0 -38.68071 28.25435 -1.369 0.6761
Herbicide: Clarity - Hand == 0 -36.70162 26.67552 -1.376 0.6715
Herbicide: Roundup Powermax - Hand == 0 -0.04304 26.18454 -0.002
1.0000
Herbicide: Clarity - Aatrex == 0 1.97908 30.07986 0.066 1.0000
Herbicide: Roundup Powermax - Aatrex == 0 38.63767 27.23358 1.419
0.6428
Herbicide: Roundup Powermax - Clarity == 0 36.65858 27.57676 1.329
0.7020
Time: T3 - T2 == 0 115.25748 36.88812 3.125 0.0155 * — Signif. codes: 0
‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported – single-step method)
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucC52YWxzXG5gYGBcbmBgYCJ9 -->
```r
```r
p.vals
<!-- rnb-source-end -->
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[[1]] Type III Analysis of Variance Table with Satterthwaite’s method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Herbicide 25385 6346.4 4 26 1.6593 0.18964
Total_Weed_Veg 19000 19000.3 1 26 4.9677 0.03468 * BG_T1 9609 9609.3 1
26 2.5124 0.12504
Time 4002 4001.7 1 26 1.0462 0.31580
Herbicide:Time 40491 10122.7 4 26 2.6466 0.05614 . — Signif. codes: 0
‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]] Type III Analysis of Variance Table with Satterthwaite’s method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Herbicide 262.85 65.71 4 26 0.3942 0.8109 Total_Weed_Veg 430.25 430.25 1 26 2.5808 0.1202 CBH_T1 17.01 17.01 1 26 0.1020 0.7520 Time 206.38 206.38 1 26 1.2379 0.2761 Herbicide:Time 754.04 188.51 4 26 1.1307 0.3638
[[3]] Type III Analysis of Variance Table with Satterthwaite’s method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Herbicide 5722.2 1430.55 4 26 2.6247 0.05765 . Total_Weed_Veg 678.0
678.03 1 26 1.2440 0.27491
PHOS_T1 2415.2 2415.21 1 26 4.4312 0.04510 * Time 536.8 536.80 1 26
0.9849 0.33014
Herbicide:Time 5896.6 1474.16 4 26 2.7047 0.05234 . — Signif. codes: 0
‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[4]] Type III Analysis of Variance Table with Satterthwaite’s method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Herbicide 425.31 106.327 4 26 1.0679 0.3923 Total_Weed_Veg 136.01 136.009 1 26 1.3660 0.2531 NAG_T1 9.91 9.910 1 26 0.0995 0.7549 Time 46.48 46.477 1 26 0.4668 0.5005 Herbicide:Time 111.05 27.763 4 26 0.2788 0.8890
[[5]] Type III Analysis of Variance Table with Satterthwaite’s method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Herbicide 1172.13 293.03 4 26 0.5487 0.7016 Total_Weed_Veg 932.54 932.54 1 26 1.7462 0.1979 BX_T1 374.48 374.48 1 26 0.7012 0.4100 Time 547.69 547.69 1 26 1.0256 0.3205 Herbicide:Time 1393.47 348.37 4 26 0.6523 0.6304
[[6]] Type III Analysis of Variance Table with Satterthwaite’s method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Herbicide 342.65 85.66 4 26 0.2028 0.9345 Total_Weed_Veg 261.13 261.13 1 26 0.6183 0.4388 AG_T1 58.86 58.86 1 26 0.1394 0.7119 Time 323.57 323.57 1 0 0.7661 1.0000 Herbicide:Time 452.82 113.21 4 26 0.2680 0.8958
[[7]] Type III Analysis of Variance Table with Satterthwaite’s method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Herbicide 1364.7 341.2 4 24 0.2533 0.90477
Total_Weed_Veg 7053.2 7053.2 1 24 5.2371 0.03121 LAP_T1 1347.9
1347.9 1 24 1.0009 0.32709
Time 1054.9 1054.9 1 24 0.7833 0.38491
Herbicide:Time 18581.2 4645.3 4 24 3.4492 0.02311 — Signif. codes:
0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Weed cover
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```r
```r
summary(glht(t3_mod, mcp(Herbicide = \Tukey\)))
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG5cdCBTaW11bHRhbmVvdXMgVGVzdHMgZm9yIEdlbmVyYWwgTGluZWFyIEh5cG90aGVzZXNcblxuTXVsdGlwbGUgQ29tcGFyaXNvbnMgb2YgTWVhbnM6IFR1a2V5IENvbnRyYXN0c1xuXG5cbkZpdDogZ2xtKGZvcm11bGEgPSBOTzNfbWdfa2dkcnlzb2lsIH4gSGVyYmljaWRlLCBkYXRhID0gVGltZTMpXG5cbkxpbmVhciBIeXBvdGhlc2VzOlxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgRXN0aW1hdGUgU3RkLiBFcnJvciB6IHZhbHVlIFByKD58enwpICBcbkhhbmQgLSBOb24tVHJlYXRlZCA9PSAwICAgICAgICAgICAgICAtMC4zMzExICAgIDIxLjc4MDEgIC0wLjAxNSAgIDEuMDAwMCAgXG5BYXRyZXggLSBOb24tVHJlYXRlZCA9PSAwICAgICAgICAgICAgIDYuMjQxNyAgICAyMS43ODAxICAgMC4yODcgICAwLjk5ODUgIFxuQ2xhcml0eSAtIE5vbi1UcmVhdGVkID09IDAgICAgICAgICAgIDI5Ljg4NzcgICAgMjEuNzgwMSAgIDEuMzcyICAgMC42NDUxICBcblJvdW5kdXAgUG93ZXJtYXggLSBOb24tVHJlYXRlZCA9PSAwICA1Ny42MTgyICAgIDIzLjUyNTIgICAyLjQ0OSAgIDAuMTAyMiAgXG5BYXRyZXggLSBIYW5kID09IDAgICAgICAgICAgICAgICAgICAgIDYuNTcyOCAgICAyMS43ODAxICAgMC4zMDIgICAwLjk5ODIgIFxuQ2xhcml0eSAtIEhhbmQgPT0gMCAgICAgICAgICAgICAgICAgIDMwLjIxODkgICAgMjEuNzgwMSAgIDEuMzg3ICAgMC42MzUzICBcblJvdW5kdXAgUG93ZXJtYXggLSBIYW5kID09IDAgICAgICAgICA1Ny45NDkzICAgIDIzLjUyNTIgICAyLjQ2MyAgIDAuMDk4OSAuXG5DbGFyaXR5IC0gQWF0cmV4ID09IDAgICAgICAgICAgICAgICAgMjMuNjQ2MSAgICAyMS43ODAxICAgMS4wODYgICAwLjgxMzcgIFxuUm91bmR1cCBQb3dlcm1heCAtIEFhdHJleCA9PSAwICAgICAgIDUxLjM3NjUgICAgMjMuNTI1MiAgIDIuMTg0ICAgMC4xODUzICBcblJvdW5kdXAgUG93ZXJtYXggLSBDbGFyaXR5ID09IDAgICAgICAyNy43MzA0ICAgIDIzLjUyNTIgICAxLjE3OSAgIDAuNzYzMyAgXG4tLS1cblNpZ25pZi4gY29kZXM6ICAwIOKAmCoqKuKAmSAwLjAwMSDigJgqKuKAmSAwLjAxIOKAmCrigJkgMC4wNSDigJgu4oCZIDAuMSDigJgg4oCZIDFcbihBZGp1c3RlZCBwIHZhbHVlcyByZXBvcnRlZCAtLSBzaW5nbGUtc3RlcCBtZXRob2QpXG4ifQ== -->
Simultaneous Tests for General Linear HypothesesMultiple Comparisons of Means: Tukey Contrasts
Fit: glm(formula = NO3_mg_kgdrysoil ~ Herbicide, data = Time3)
Linear Hypotheses: Estimate Std. Error z value Pr(>|z|)
Hand - Non-Treated == 0 -0.3311 21.7801 -0.015 1.0000
Aatrex - Non-Treated == 0 6.2417 21.7801 0.287 0.9985
Clarity - Non-Treated == 0 29.8877 21.7801 1.372 0.6451
Roundup Powermax - Non-Treated == 0 57.6182 23.5252 2.449 0.1022
Aatrex - Hand == 0 6.5728 21.7801 0.302 0.9982
Clarity - Hand == 0 30.2189 21.7801 1.387 0.6353
Roundup Powermax - Hand == 0 57.9493 23.5252 2.463 0.0989 . Clarity -
Aatrex == 0 23.6461 21.7801 1.086 0.8137
Roundup Powermax - Aatrex == 0 51.3765 23.5252 2.184 0.1853
Roundup Powermax - Clarity == 0 27.7304 23.5252 1.179 0.7633
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05
‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported – single-step method) ```
---
title: "GLM enzymes with global test and dunnetts"
output: html_notebook
---

```{r}
library(tidyverse)
library(MASS)
library(multcomp)
library(tidyr)
library(vegan)
library(parallelDist)
library(ggordiplots)
library(lmtest)
library(psych)
library(lme4)
library(nlme)
library(multcomp)
```
Read in data
```{r}
total<-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/merged_metadata_herbicide_EPSCOR_2018.csv")
enz <- read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Enzymes/Enzyme_Summary_W_percent_Change.csv")
enz_only<-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Enzymes/Enzyme.csv")

wide_total <-read.csv("/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Herbicide_SAREC_2018/Wide_merged_GLM.csv")
```

Split data by herbicide and time so we can look at each individually
```{r}
#enz_only$Herbicide<-factor(enz_only$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = T)
enz_only$Herbicide<-factor(enz_only$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
str(enz_only$Total_Weed_Veg)
#checks out its numeric here. 

Aatrex<-filter(enz_only, Herbicide == "Aatrex")
Clarity<-filter(enz_only, Herbicide == "Clarity")
Roundup<-filter(enz_only, Herbicide == "Roundup Powermax")

Handweed<-filter(enz_only, Herbicide != c("Aatrex"))
Handweed<-filter(Handweed, Herbicide != c("Roundup Powermax"))
Handweed<-filter(Handweed, Herbicide != c("Clarity"))
Handweed<-filter(Handweed, Herbicide != c("Non-Treated"))

NonTreat<-filter(enz_only, Herbicide == "Non-Treated")

  
Time1<-filter(enz_only, Time == "T1")
Time2<-filter(enz_only, Time == "T2")
Time3<-filter(enz_only, Time == "T3")
Time2_3<-filter(enz_only, Time != "T1")
Time1_2<-filter(enz_only, Time != "T3")
```

Create summary tables
```{r}
sub_enz_data<-enz_only[,c("Herbicide", "Time", "BG", "BX", "CBH", "AG", "NAG", "LAP", "PHOS" )]
enz_summary<-describeBy(sub_enz_data, list(sub_enz_data$Herbicide, sub_enz_data$Time), mat = T)

#write.csv(enz_summary, "/Users/gordoncuster/Desktop/Manuscript Submissions/Herbicide and Microbes/Writing/Tables/enz_summary.csv")
#IC_summary2<-do.call("rbind", IC_summary)
```

glm global test of enzymes time 1 against handweeded plots
F stat for overall model signficance compared to intercept only model. Only if the more complex model is signficnat are the predictors kept. 
#https://statisticsbyjim.com/regression/interpret-f-test-overall-significance-regression/
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time1 <- Time1[!is.na(Time1$LAP),]
#sets order for handweed to be first category so dunnets compares to this. 
sub_Time1$Herbicide<-factor(sub_Time1$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}

models_out
p.predict
out


```

glm global test of enzymes time 1 agaist non-treated plots
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time1 <- Time1[!is.na(Time1$LAP),]
sub_Time1$Herbicide<-factor(sub_Time1$Herbicide, levels = c( "Non-Treated", "Hand",  "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time1)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}

models_out
p.predict
out

#drop1(mod_herb, test = "F")
```

adonis2 and graphs
```{r}
#convert to long format for ggplot
subtime1_long <- sub_Time1[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )

data_long<-enz_only[,c("Herbicide", "Time","BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>% 
  gather(key = "Enzyme", value = "Activity", BG, CBH, PHOS, NAG, BX, AG, LAP )

dose.labs <- c("Time one","Time two","Time three")
names(dose.labs) <- c("T1", "T2", "T3")

phos_T1<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="PHOS")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y")  +
  theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) + ylim(50,180) + theme(legend.position="none") +  theme(strip.text.x = element_text(size =  12)) +ggtitle("PHOS Activity")
                                                                                                                                                                                                                                                                 
bgTime<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="BG")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y")  +
  theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) + theme(legend.position="none") +  theme(strip.text.x = element_text(size =  12))+ggtitle("BG Activity")

lapTime<-ggplot(data_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(data_long,Enzyme=="LAP")) + theme_classic() + facet_wrap(.~ Time, labeller = labeller(Time = dose.labs)) +
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y")  +
  theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c( "Nontreated", "Handweeded", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=13)) #+ theme(legend.position="none") +  theme(strip.text.x = element_text(size =  12))+ggtitle("LAP Activity")

grid.arrange(phos_T1, bgTime, lapTime)

#multivariate
enz_table<-sub_Time1[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time1[,c("Herbicide", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

time1_ord<-metaMDS(na.omit(sub_Time1[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(time1_ord)

ord<-time1_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Herbicide")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Herbicide)  + scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) + geom_point(aes(color= Herbicide))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
    geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0) 
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22))  + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) 
g
```
Time 2 comparison to handweed
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time2 <- Time2[!is.na(Time2$LAP),]
sub_Time2$Herbicide<-factor(sub_Time2$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}


models_out
p.predict
out
```

Time 2 comparison to non-treated
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time2 <- Time2[!is.na(Time2$LAP),]
sub_Time2$Herbicide<-factor(sub_Time2$Herbicide, levels = c(  "Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time2)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}


models_out
p.predict
out
```

```{r}
#convert to long format for ggplot
subTime2_long <- sub_Time2[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )

BG_T2<-ggplot(subTime2_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subtime1_long,Enzyme=="BG")) + theme_classic() + #facet_wrap(~ Enzyme, scales = "free_y") +
   ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Weed removal treatment") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) 


phos_T2<-ggplot(subTime2_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subTime2_long,Enzyme=="PHOS")) + theme_classic()+
#ggplot(subtime1_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot() +theme_classic() + facet_wrap(~ Enzyme, scales = "free_y")  +
  theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("")  + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 14))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + ylim(50,180)+ theme(legend.position="none") + xlab("Weed removal treatment")

#multivariate
enz_table<-sub_Time2[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time2[,c("Herbicide", "Treatment", "Total_Weed_Veg", "LQ_PW", "PW_Tot")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Time2_ord<-metaMDS(na.omit(sub_Time2[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Time2_ord)



ord<-Time2_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Total_Weed_Veg, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
test$LQ_PW<-samp_data$LQ_PW
test$PW_Tot<-samp_data$PW_Tot
#test$Group=NULL
names(test)<-c("x", "y","Total Weedy Vegetation", "Sample_ID", "LQ_PW" , "PW_Tot")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y, size=`Total Weedy Vegetation`, color=PW_Tot))  +# scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) + 
geom_point()
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
    geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0) 
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22))  + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) 
g
```
Time 3 comparison to handweed 
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time3 <- Time2[!is.na(Time3$LAP),]
sub_Time3$Herbicide<-factor(sub_Time3$Herbicide, levels = c( "Hand", "Non-Treated", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}


models_out
p.predict
out
```
Time 3 to non-treated
```{r}
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Time3 <- Time2[!is.na(Time3$LAP),]
sub_Time3$Herbicide<-factor(sub_Time3$Herbicide, levels = c(  "Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Time3)
  ##3rd model? 
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`[2], glob_sig$Df[2], glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF", "F", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Herbicide="Dunnett")))
         # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}


models_out
p.predict
out
```

```{r}
#convert to long format for ggplot
subTime3_long <- sub_Time3[,c("Herbicide", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )

PHOS_T3<-ggplot(subTime3_long, aes_string(x = "Herbicide", y= "Activity", fill="Herbicide")) + geom_boxplot(data=subset(subtime1_long,Enzyme=="PHOS")) + theme_classic() + #facet_wrap(~ Enzyme, scales = "free_y") +
   ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("") + xlab("") + scale_fill_discrete(name = "Weed removal\n treatment", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15)) + ylim(50,180) + theme(legend.position="none")



#multivariate
enz_table<-sub_Time3[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Time3[,c("Herbicide", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Herbicide + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Time3_ord<-metaMDS(na.omit(sub_Time3[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Time3_ord)

ord<-Time3_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Herbicide, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "Hand", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "Non-Treated", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "Aatrex", droplevels= TRUE)
ellipseG4 <- subset(df_ellipse, Group == "Clarity", droplevels= TRUE)
ellipseG5 <- subset(df_ellipse, Group == "Roundup Powermax", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Herbicide")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Herbicide)  + scale_color_manual(values=c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")) + geom_point(aes(color= Herbicide))


#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#A3A500", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#00BF7D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG4, aes(x=x, y=y), color="#00B0F6", linetype="solid", size=1.0) +
    geom_path(data= ellipseG5, aes(x=x, y=y), color="#E76BF3", linetype="solid", size=1.0) 
#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22))  + scale_fill_discrete(name = "Herbicide", labels = c("Handweeded", "Nontreated", "Aatrex", "Clarity", "Roundup\nPowermax")) 
g
```


```{r}
library(gridExtra)
grid.arrange(phos_T1, phos_T2, PHOS_T3, nrow=1)
```


#Split by herbicide

All Together:
```{r}
#Aatrex
#names(Aatrex)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
#Aatrex$Herbicide<-factor(Aatrex$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[!sub_enz_only$Time=="T1",]


#convert to long format for ggplot
subenz_long <- sub_enz_only[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP )

ggplot(subenz_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("All samples by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))

#multivariate
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Treatment + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Aatrex_ord<-metaMDS(na.omit(sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Aatrex_ord)

ord<-Aatrex_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Aatrex by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))  
g


#adonis2 testing and pairwise comparisons for all t2 t3 samples 
library(pairwiseAdonis)
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
#sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[!sub_enz_only$Time=="T1",]
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment")]
samp_data$Treatment<-as.factor(samp_data$Treatment)
psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Treatment, data = data.frame(samp_data), permutations = 1000)
pairwise_treat<-pairwise.adonis2(psdist ~ Treatment, data = samp_data, perm=1000, p.adjust.methods="bonferroni")


#examination of changes over time. 
sub_enz_only <- enz_only[!is.na(enz_only$LAP),]
#sub_enz_only <- sub_enz_only[!sub_enz_only$Herbicide=="Non-Treated",]
sub_enz_only <- sub_enz_only[sub_enz_only$Herbicide=="Hand",]
enz_table<-sub_enz_only[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_enz_only[,c("Time", "Treatment")]
psdist<-parDist(as.matrix(enz_table), method = "bray")
adonis2(psdist ~ Time , data = data.frame(samp_data), permutations = 1000)
pairwise_time<-pairwise.adonis2(psdist ~ Time, data = samp_data, perm=1000, p.adjust.methods="bonferroni")
pairwise_time
```


Aatrex with global test. 
```{r}
#Aatrex
#names(Aatrex)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
#Aatrex$Herbicide<-factor(Aatrex$Herbicide, levels = c("Non-Treated", "Hand", "Aatrex", "Clarity", "Roundup Powermax"), ordered = F)
#univariate
sub_Aatrex <- Aatrex[!is.na(Aatrex$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Aatrex)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Aatrex)
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
    # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}
models_out
p.predict
out

#convert to long format for ggplot
subAatrex_long <- sub_Aatrex[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP, C.N_enz )

#ggplot(subAatrex_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Aatrex by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))

dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")

aaUV1<-ggplot(subAatrex_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
   ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time")  + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))  +  theme(strip.text.x = element_text(size =  12)) #theme(legend.position="none") + 

#multivariate
enz_table<-sub_Aatrex[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Aatrex[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Aatrex_ord<-metaMDS(na.omit(sub_Aatrex[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Aatrex_ord)

ord<-Aatrex_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Aatrex by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))  
g
```

Clarity
```{r}
#Clarity
#names(Clarity)
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Clarity <- Clarity[!is.na(Clarity$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Clarity)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Clarity)
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
    # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}
models_out
p.predict
out

#convert to long format for ggplot
subClarity_long <- sub_Clarity[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP,C.N_enz )

#ggplot(subClarity_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Clarity by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))


dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")

clUV1<-ggplot(subClarity_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
   ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time")  + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))  +  theme(strip.text.x = element_text(size =  12)) #theme(legend.position="none") + 

#multivariate
enz_table<-sub_Clarity[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Clarity[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Clarity_ord<-metaMDS(na.omit(sub_Clarity[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Clarity_ord)

ord<-Clarity_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Clarity by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))  
g
```

Handweed
```{r}
#Handweed
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Handweed <- Handweed[!is.na(Handweed$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Handweed)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Handweed)
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
    # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}
models_out
p.predict
out
#convert to long format for ggplot
subHandweed_long <- sub_Handweed[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity",AG,BG, PHOS, LAP, BX, C.N_enz )

HW_UV<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1))  + ggtitle("Handweed by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))

subHandweed_long$Enzyme<-factor(subHandweed_long$Enzyme, levels = c("AG", "BG", "BX", "PHOS", "LAP", "C.N_enz"))

dose.labs <- c("AG","BG","BX","PHOS", "LAP",  "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "LAP", "C.N_enz")

HW_UV1<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
   ggtitle("Handweeded") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time")  + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))  +  theme(strip.text.x = element_text(size =  12)) #theme(legend.position="none") + 

#multivariate
enz_table<-sub_Handweed[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Handweed[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

pairwise_time_handweed<-pairwise.adonis2(psdist ~ Time, data = samp_data, perm=1000, p.adjust.methods="bonferroni")
pairwise_time_handweed

Handweed_ord<-metaMDS(na.omit(sub_Handweed[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Handweed_ord)

ord<-Handweed_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F8766D", "#00BA38", "#619CFF", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#00BA38", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
m <- m + annotate("text", label="Sampling time:\np < 0.01", x = 0.12, y = -0.055)
g <- m + ggtitle("") + theme(plot.title = element_text(hjust = 0.5, size = 22))  + theme(axis.title.x = element_text(size=15)) + theme(axis.title.y = element_text(size=15)) + scale_fill_discrete(name = "Sampling time", labels = c("Time 1", "Time 2", "Time 3"))
g

HW_MV<-g

```

```{r}
#Roundup
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_Roundup <- Roundup[!is.na(Roundup$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_Roundup)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_Roundup)
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
    # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}
models_out
p.predict
out

#convert to long format for ggplot
subRoundup_long <- sub_Roundup[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity", AG, BG, BX, PHOS, LAP, C.N_enz )

subRoundup_long$Enzyme<-factor(subRoundup_long$Enzyme, levels = c("AG", "BG", "BX", "PHOS", "LAP", "C.N_enz"))

dose.labs <- c("AG","BG","BX","PHOS", "LAP",  "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "LAP", "C.N_enz")

RUUV1<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
   ggtitle("Roundup Powermax") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time")  + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))  +  theme(strip.text.x = element_text(size =  12)) #theme(legend.position="none") + 

#ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Roundup Powermax by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))


Phos_RU<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot(data=subset(subRoundup_long, subRoundup_long["Enzyme"] == "PHOS" )) + theme_classic() + #facet_wrap(~ Edaphic, scales = "free_y") + 
  theme(axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 15)) + ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 20)) + theme(axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 12))+ ylab("Activity\n(nmol/hr/g dry soil)") + xlab("Sampling Time") + scale_fill_discrete(name = "Sampling time", labels = c("Time 1", "Time 2", "Time 3"))

#multivariate
enz_table<-sub_Roundup[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_Roundup[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

Roundup_ord<-metaMDS(na.omit(sub_Roundup[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(Roundup_ord)

ord<-Roundup_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("Roundup by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))  
g
```

```{r}
#NonTreat
enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
sub_NonTreat <- NonTreat[!is.na(NonTreat$LAP),]
#univariate
out<-list()
models_out<-list()
p.predict<-list()

for(i in 1:(length(enzymes))){
  mod_herb <- glm(formula = noquote(paste(enzymes[i], "~ Time + Total_Weed_Veg", sep = "")), family = gaussian(link = "identity"), data = sub_NonTreat)
  mod_inter <- glm(formula = noquote(paste(enzymes[i], "~ 1", sep = "")), family = gaussian(link = "identity"), data = sub_NonTreat)
  glob_sig<-anova(mod_inter, mod_herb, test = "F")
    if(glob_sig$`Pr(>F)`[2]<=0.05){
        test_stats<-c(glob_sig$`Resid. Df`, glob_sig$F[2], glob_sig$`Pr(>F)`[2])
        names(test_stats) <- c("DF_res", "DF_null", "F-stat", "p-val")
        sum_model<-summary(mod_herb)
        post_hoc<-summary(glht(mod_herb, mcp(Time="Tukey")))
    # p<-anova(mod_herb, mod_inter)
        #  p.vals<-c(p.vals, list(p))
        significance_predic<-drop1(mod_herb, test = "F")
        out<-c(out, list(post_hoc), enzymes[i])
        models_out <- c(models_out, enzymes[i], list(test_stats), list(sum_model))
        p.predict <- c(p.predict, list(significance_predic), enzymes[i])
    }

}
models_out
p.predict
out

#convert to long format for ggplot
subNonTreat_long <- sub_NonTreat[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity",BG, CBH, PHOS, NAG, BX, AG, LAP, C.N_enz )


dose.labs <- c("AG","BG","BX","PHOS", "C enzymes : N enzymes")
names(dose.labs) <- c("AG","BG", "BX", "PHOS", "C.N_enz")

aaUV1<-ggplot(subNonTreat_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y", labeller = labeller(Enzyme = dose.labs)) + theme_classic() +
   ggtitle("") + theme(plot.title= element_text( hjust = 0.5, size = 22)) + ylab("Activity (nmol/h/g)") + xlab("Sampling time")  + scale_fill_discrete(name = "Sampling time", labels = c("Time one", "Time two", "Time three")) + theme(axis.text.y = element_text(size=12))  + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + 
       theme(strip.text.x = element_text(size = 9, colour = "black", angle = 0)) + theme(axis.title.y = element_text(size=15))  +  theme(strip.text.x = element_text(size =  12)) #theme(legend.position="none") + 


#ggplot(subNonTreat_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Non-treated by time") + theme(plot.title= element_text( hjust = 0.5, size = 22))


#multivariate
enz_table<-sub_NonTreat[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]
samp_data <- sub_NonTreat[,c("Time", "Treatment", "Total_Weed_Veg")]

psdist<-parDist(as.matrix(enz_table), method = "bray")

adonis2(psdist ~ Time + Total_Weed_Veg, data = data.frame(samp_data), permutations = 1000)

NonTreat_ord<-metaMDS(na.omit(sub_NonTreat[,c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")]), distance = "bray" )
stressplot(NonTreat_ord)

ord<-NonTreat_ord
#make the plot with everything 
d <- gg_ordiplot(ord, samp_data$Time, choices = c(1, 2), kind = c("se"), conf = 0.95, show.groups = "all", ellipse = TRUE, label = FALSE, hull = FALSE, spiders = FALSE, plot = TRUE, pt.size = 1) 
#pull out the ellipses
df_ellipse <- d$df_ellipse
ellipseG1 <- subset(df_ellipse, Group == "T1", droplevels= TRUE)
ellipseG2 <- subset(df_ellipse, Group == "T2", droplevels= TRUE)
ellipseG3 <- subset(df_ellipse, Group == "T3", droplevels= TRUE)


#make the dataframe and subset out samples you dont want to plot
test<-d$df_ord
test$Sample_ID<-rownames(test)
#test$Group=NULL
names(test)<-c("x", "y","Time")

#make the ggplot scatter plot
plot_test<-ggplot(test, aes(x=test$x, y=test$y), color=Time)  + scale_color_manual(values=c("#F564E3", "#619CFF", "#F8766D", "#009E73", "#000000")) + geom_point(aes(color= Time))
#add paths, color, and size, of the hulls
plot_test <- plot_test +
    geom_path(data= ellipseG1, aes(x=x, y=y), color="#F564E3", linetype="solid", size=1.0) +
    geom_path(data= ellipseG2, aes(x=x, y=y), color="#619CFF", linetype="solid", size=1.0) +
    geom_path(data= ellipseG3, aes(x=x, y=y), color="#F8766D", linetype="solid", size=1.0) 

#theme it on up
plot_test<- plot_test + theme_classic()
m<-plot_test 
#m <-m + annotate("text", x = -0.05, y = -0.012, label = "Time 2")
#m <-m + annotate("text", x = 0.01, y = -0.01, label = "Time 3")
#m <-m + annotate("text", x = 0.045, y = -0.023, label = "Time 1")
#m <-m + annotate("text", x = 0.4, y = -0.5, label = "Time: P  > 0.8")
m<- m + labs(x= "NMDS2", y="NMDS1")
g <- m + ggtitle("NonTreat by time") + theme(plot.title = element_text(hjust = 0.5, size = 22))  
g
```


combine figures for manuscirpt
```{r}

subRoundup_long <- sub_Roundup[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity", AG, BG, BX, PHOS, C.N_enz )

RU_UV<-ggplot(subRoundup_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + ggtitle("Glyphosate") + theme(plot.title= element_text( hjust = 0.5, size =17))  + ylab("") + xlab("") + scale_fill_discrete(name = "Sampling time", labels = c("Pre-treatment", "10-days post", "20-days post"))  + theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  + theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) + theme(axis.text.y = element_text(size=14, angle=45)) + theme(axis.title.y = element_text(size=15))

subHandweed_long <- sub_Handweed[,c("Time", "BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP", "C.N_enz")] %>% 
  gather(key = "Enzyme", value = "Activity",, AG, BG, BX, PHOS, C.N_enz )

HW_UV<-ggplot(subHandweed_long, aes_string(x = "Time", y= "Activity", fill="Time")) + geom_boxplot() + theme_classic() + facet_wrap(~ Enzyme, scales = "free_y") + theme(axis.text.x = element_text(angle = 45, hjust = 1))  + ggtitle("Handweeded") + theme(plot.title= element_text( hjust = 0.5, size = 17))  + ylab("") + xlab("") + scale_fill_discrete(name = "Sampling time", labels = c("Pre-treatment", "10 days post", "20 days post"))  + theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.title.x = element_text(size = 12))  +  theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0))  + theme(axis.text.y = element_text(size=14, angle=45)) + theme(axis.title.y = element_text(size=15)) + theme(legend.position = "none") 


#HW_MV <- HW_MV + theme(legend.position = "none") + ggtitle("Hand-weeded") + theme(plot.title= element_text( hjust = 0.5, size = 15)) + theme(axis.text.y = element_text(size=14, angle=0)) + theme(axis.text.x = element_text(size = 14))


my_legend <- get_legend(RU_UV)
as_ggplot(my_legend)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity_legend.pdf")
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity_legend.eps")
RU_UV<-RU_UV+ theme(legend.position = "none") 
RU_UV


ggarrange(HW_UV, RU_UV,  nrow = 2, ncol = 1)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity.eps", width = 7, height = 10)
ggsave("/Users/gordoncuster/Desktop/Git_Projects/Herbicide_Microbes_PT1/Figures/Enz_hump_activity.pdf", width = 7, height = 10)

```


#All together with conservative mixed models

with time 1 values as a preditor and only time 2 and 3
```{r}
enz_only$block<-factor(substr(enz_only$Sample_ID, start = 1, stop = 1))
dat <- enz_only[!is.na(enz_only$LAP),]

#Good for shapiro test on residuals without log transform. I ran each model and checked the residuals for normailty using "shapiro.test(resid(test_model_lmer))". All of the follwing passed the shapiro wilk test : BG, CBH, AG, BX, PHOS, LAP, NAG

dat<-dat[dat$Time != "T1", ]

library(lmerTest)
test_model_lmer<-lmer(NAG ~ Herbicide + BG_T1 + Time + (1|block) + (1|Time), data = dat)
summary(test_model_lmer)
summary(glht(test_model_lmer, mcp(Herbicide = "Tukey", Time = "Tukey")))
anova(test_model_lmer)

enzymes<- c("BG", "CBH", "PHOS", "NAG", "BX", "AG", "LAP")
enzymes_t1<-c("BG_T1", "CBH_T1", "PHOS_T1", "NAG_T1", "BX_T1", "AG_T1", "LAP_T1")
dat
#univariate
out<-list()
models_out<-list()
p.vals<-list()

for(i in 1:(length(enzymes))){
  mod <- lmer(formula = noquote(paste(enzymes[i], "~ Herbicide + Total_Weed_Veg +", enzymes_t1[i], "+ Time + Time*Herbicide + (1|Time)", sep = "")), data = dat)
  sum_model<-summary(mod)
  post_hoc<-summary(glht(mod, mcp(Herbicide = "Tukey", Time = "Tukey")))
  p<-anova(mod)
  p.vals<-c(p.vals, list(p))
  out<-c(out, list(post_hoc))
  models_out <- c(models_out, list(sum_model))
}


models_out
out
p.vals
```



Weed cover
```{r}
t1_mod<-glm(Total_Weed_Veg ~ Herbicide, data = Time1)
summary(glht(t1_mod, mcp(Herbicide = "Tukey")))
plot(Time1$Total_Weed_Veg ~ Time1$Herbicide)


t2_mod<-glm(Total_Weed_Veg ~ Herbicide, data = Time2)
summary(glht(t2_mod, mcp(Herbicide = "Tukey")))
plot(Time2$Total_Weed_Veg ~ Time2$Herbicide)
t3_mod<-glm(NO3_mg_kgdrysoil ~ Total_Weed_Veg, data = Time2)
summary(t3_mod)

t3_mod<-glm(NO3_mg_kgdrysoil ~ Herbicide, data = Time3)
summary(t3_mod)
summary(glht(t3_mod, mcp(Herbicide = "Tukey")))
plot(Time3$Total_Weed_Veg ~ Time3$Herbicide)
```

